{
  "version": "1.0",
  "created": "2026-02-03",
  "categories": {
    "rag-architecture": {
      "name": "RAG Architecture & Semantic Search",
      "dir": "rag-architecture",
      "priority": "HIGH",
      "skills": [
        {
          "id": "rag-core-patterns",
          "title": "RAG Core Patterns and Architecture",
          "description": "Grunnleggende RAG-mønstre, når de brukes, og arkitektoniske varianter (naiv, avansert, agentic RAG).",
          "subtopics": [
            "RAG flow overview",
            "Naive vs advanced RAG",
            "Agentic RAG",
            "In-context learning",
            "Long-context models"
          ]
        },
        {
          "id": "azure-ai-search-setup",
          "title": "Azure AI Search - Configuration and Deployment",
          "description": "Oppsett, skalering, og deployment av Azure AI Search med fokus på performance og kostnader.",
          "subtopics": [
            "Tiers and SKU selection",
            "Indexing strategies",
            "Search service configuration",
            "Pricing models",
            "Scaling considerations"
          ]
        },
        {
          "id": "embedding-models-selection",
          "title": "Embedding Models - Selection and Optimization",
          "description": "Valg av embedding-modell, dimensjonalitet, og optimering for ulike domener og use cases.",
          "subtopics": [
            "Model comparison",
            "Dimensionality trade-offs",
            "Domain-specific embeddings",
            "Multilingual embeddings",
            "Cost vs quality"
          ]
        },
        {
          "id": "vector-indexing-techniques",
          "title": "Vector Indexing - Techniques and Configuration",
          "description": "Vektorindeksering med fokus på hybrid search, HierarchicalNSW, og reranking i Azure AI Search.",
          "subtopics": [
            "Hybrid search setup",
            "Vector search algorithms",
            "Index configuration",
            "Performance tuning",
            "Batch indexing"
          ]
        },
        {
          "id": "chunking-strategies",
          "title": "Document Chunking - Strategies and Implementation",
          "description": "Optimal chunking av dokumenter for RAG, including overlap, chunk size, og semantisk chunking.",
          "subtopics": [
            "Fixed-size chunking",
            "Semantic chunking",
            "Overlap strategies",
            "Parent-child chunking",
            "Chunk metadata"
          ]
        },
        {
          "id": "hybrid-search-configuration",
          "title": "Hybrid Search - Full-Text and Vector Combined",
          "description": "Kombinering av BM25 full-text search med vector search, weighted scoring, og relevance tuning.",
          "subtopics": [
            "BM25 tuning",
            "Weight balancing",
            "Query expansion",
            "Relevance scoring",
            "A/B testing"
          ]
        },
        {
          "id": "semantic-ranker-reranking",
          "title": "Semantic Ranker and Reranking Models",
          "description": "Bruk av Microsoft Semantic Ranker og tredjeparts reranking-modeller for å forbedre resultatrekkefølge.",
          "subtopics": [
            "Semantic Ranker setup",
            "Reranking models",
            "Cross-encoder usage",
            "List-wise ranking",
            "Performance impact"
          ]
        },
        {
          "id": "citation-tracking",
          "title": "Citation Tracking and Source Attribution",
          "description": "Implementering av citation tracking, source mapping, og verifiable references i RAG-output.",
          "subtopics": [
            "Source tracking",
            "Citation formatting",
            "Provenance tracking",
            "Confidence scoring",
            "Hallucination prevention"
          ]
        },
        {
          "id": "rag-evaluation-frameworks",
          "title": "RAG Evaluation Metrics and Frameworks",
          "description": "Evaluering av RAG-systemer med fokus på retrieval quality, relevance, og generation fidelity.",
          "subtopics": [
            "Retrieval metrics (MRR, NDCG)",
            "Generation metrics (ROUGE, BLEU)",
            "Semantic similarity",
            "Human evaluation",
            "Baseline comparison"
          ]
        },
        {
          "id": "multi-index-federation",
          "title": "Multi-Index Federation and Cross-Search",
          "description": "Arkitektur for spørring på tvers av flere indekser, ranking, og resultat-aggregasjon.",
          "subtopics": [
            "Multi-index design",
            "Cross-index ranking",
            "Result merging",
            "Query routing",
            "Performance optimization"
          ]
        },
        {
          "id": "rag-security-rbac",
          "title": "RAG Security - RBAC, Filtering, and Access Control",
          "description": "Sikkerhet i RAG med fokus på dokumentnivå RBAC, content filtering, og tilgangscontrol.",
          "subtopics": [
            "Document-level RBAC",
            "Security filters",
            "User context filtering",
            "Compliance requirements",
            "Audit logging"
          ]
        },
        {
          "id": "rag-caching-optimization",
          "title": "RAG Caching and Performance Optimization",
          "description": "Caching-strategier for RAG-komponenter, query result caching, og latency-reduksjon.",
          "subtopics": [
            "Query caching",
            "Embedding caching",
            "Index caching",
            "TTL strategies",
            "Cache invalidation"
          ]
        },
        {
          "id": "metadata-management-filtering",
          "title": "Metadata Management and Filtered Search",
          "description": "Organisering og bruk av metadata for avansert filtrering og faceted search i RAG.",
          "subtopics": [
            "Metadata schema design",
            "OData filtering",
            "Faceted navigation",
            "Date range filtering",
            "Category hierarchies"
          ]
        },
        {
          "id": "graphrag-knowledge-graphs",
          "title": "GraphRAG - Knowledge Graphs and Relationship Extraction",
          "description": "Bruk av knowledge graphs i RAG for å øke relevanssøk via entitets- og relasjonsforbindelser.",
          "subtopics": [
            "Entity extraction",
            "Relationship graphs",
            "Graph indexing",
            "Traversal queries",
            "Entity linking"
          ]
        },
        {
          "id": "rag-query-understanding",
          "title": "Query Understanding and Expansion",
          "description": "Teknikker for å forbedre spørsmål før søk, inkludert query expansion, intent detection, og reformulation.",
          "subtopics": [
            "Intent classification",
            "Query expansion",
            "Query rewriting",
            "Sub-query decomposition",
            "Contextual refinement"
          ]
        },
        {
          "id": "rag-context-windows",
          "title": "RAG Context Windows and Long-Context Models",
          "description": "Optimering av kontext-størrelse, token-budsjetter, og bruk av long-context modeller i RAG.",
          "subtopics": [
            "Context window sizing",
            "Token budget allocation",
            "Prompt compression",
            "Lost-in-the-middle effect",
            "Long-context LLMs"
          ]
        },
        {
          "id": "streaming-rag-responses",
          "title": "Streaming and Real-Time RAG Responses",
          "description": "Implementering av streaming-output i RAG for lavere latency og bedre brukeropplevelse.",
          "subtopics": [
            "Stream implementation",
            "Chunked responses",
            "Progressive rendering",
            "Token-by-token updates",
            "Connection management"
          ]
        },
        {
          "id": "rag-iterative-refinement",
          "title": "Iterative RAG and Multi-Turn Refinement",
          "description": "Flerturs-RAG med iterativ refinement, follow-up spørsmål, og kontekst-vedlikehold.",
          "subtopics": [
            "Conversation history management",
            "Context persistence",
            "Refinement loops",
            "Relevance feedback",
            "Session state"
          ]
        },
        {
          "id": "rag-enterprise-scale",
          "title": "RAG at Enterprise Scale - Indexing and Serving",
          "description": "Skalering av RAG for enterprise-volumer, batch processing, og serving-infrastruktur.",
          "subtopics": [
            "Batch indexing pipelines",
            "Incremental updates",
            "Distributed indexing",
            "Load balancing",
            "Disaster recovery"
          ]
        },
        {
          "id": "rag-document-preprocessing",
          "title": "Document Preprocessing and Pipeline Automation",
          "description": "Automatisert dokumentbehandling før indeksering, inkludert OCR, format-konvertering, og cleaning.",
          "subtopics": [
            "PDF and image handling",
            "Format conversion",
            "Text cleaning",
            "OCR integration",
            "Batch processing"
          ]
        },
        {
          "id": "rag-hallucination-mitigation",
          "title": "RAG Hallucination Mitigation Strategies",
          "description": "Teknikker for å redusere hallunenasjoner i RAG gjennom grounding, fact-checking, og confidence estimation.",
          "subtopics": [
            "Fact verification",
            "Confidence scoring",
            "Grounding techniques",
            "Refusal mechanisms",
            "Output validation"
          ]
        },
        {
          "id": "rag-cost-optimization",
          "title": "RAG Cost Optimization and Efficiency",
          "description": "Kostnadsoptimering av RAG-infrastruktur, embedding-modeller, og API-kall gjennom smart batching og caching.",
          "subtopics": [
            "Embedding cost reduction",
            "Query optimization",
            "Index size management",
            "Token efficiency",
            "Billing analysis"
          ]
        },
        {
          "id": "contextual-retrieval",
          "title": "Contextual Retrieval — Kontekstuell berikelse av chunks",
          "description": "Prepend LLM-generert kontekst til chunks før embedding for 35-67% bedre retrieval.",
          "subtopics": [
            "Context generation",
            "Custom skills",
            "BM25 hybrid",
            "Cost analysis"
          ]
        },
        {
          "id": "late-chunking-patterns",
          "title": "Late Chunking Patterns — Chunking etter embedding",
          "description": "Embed hele dokumenter først, chunk token-embeddings etterpå for bedre kryss-referanser.",
          "subtopics": [
            "Jina embeddings",
            "Token-level embeddings",
            "Azure integration",
            "Cost trade-offs"
          ]
        },
        {
          "id": "hierarchical-rag-patterns",
          "title": "Hierarchical RAG Patterns — Multi-nivå retrieval",
          "description": "Parent-child relasjoner og retrieval cascade for effektiv storskala RAG.",
          "subtopics": [
            "Index projections",
            "Parent-child mapping",
            "Retrieval cascade",
            "Document Layout"
          ]
        },
        {
          "id": "agentic-rag-patterns",
          "title": "Agentic RAG Patterns — Agent-styrt retrieval",
          "description": "LLM-agenter som autonomt velger retrieval-strategi og itererer til tilfredsstillende svar.",
          "subtopics": [
            "Semantic Kernel RAG",
            "Tool-based RAG",
            "Azure agentic retrieval",
            "Multi-agent"
          ]
        },
        {
          "id": "self-reflective-rag",
          "title": "Self-Reflective RAG — Selvevaluerende retrieval",
          "description": "CRAG og self-RAG med confidence scoring og iterativ forbedring via Azure AI Foundry evaluators.",
          "subtopics": [
            "CRAG patterns",
            "Azure evaluators",
            "Confidence scoring",
            "Iterative refinement"
          ]
        },
        {
          "id": "multimodal-rag",
          "title": "Multimodal RAG — Bilder, tabeller og dokumenter i RAG",
          "description": "Indekser og hent bilder, tabeller og diagrammer med Azure Vision og Content Understanding.",
          "subtopics": [
            "Image verbalization",
            "Multimodal embeddings",
            "Content Understanding",
            "Table extraction"
          ]
        }
      ]
    },
    "azure-ai-services": {
      "name": "Azure AI Services (Foundry Tools)",
      "dir": "azure-ai-services",
      "priority": "HIGH",
      "skills": [
        {
          "id": "azure-ai-vision-ocr-processing",
          "title": "Azure AI Vision - OCR and Document Processing",
          "description": "Optisk tegn gjenkjenning, håndskriftsgjenkjenning, og dokumentanalyse med Azure Computer Vision API.",
          "subtopics": [
            "OCR capabilities",
            "Handwriting recognition",
            "Document layout analysis",
            "Language detection",
            "Performance optimization"
          ]
        },
        {
          "id": "azure-ai-vision-image-analysis",
          "title": "Azure AI Vision - Image Analysis and Tagging",
          "description": "Bildegjengjøring, objektgjenkjenning, ansiktsgjenkjenning og generering av bildetagger for visuelt innhold.",
          "subtopics": [
            "Object detection",
            "Face detection and attributes",
            "Image tagging",
            "Content moderation",
            "Dense captions"
          ]
        },
        {
          "id": "document-intelligence-prebuilt-models",
          "title": "Document Intelligence - Prebuilt Models for Forms and Invoices",
          "description": "Forhåndsbyggede modeller for ekstrahering av data fra fakturaer, kvitteringer, skattedokumenter og standardskjemaer.",
          "subtopics": [
            "Invoice model",
            "Receipt model",
            "Tax document extraction",
            "Form recognition",
            "Confidence scores"
          ]
        },
        {
          "id": "document-intelligence-custom-models",
          "title": "Document Intelligence - Custom Model Training",
          "description": "Trening av egendefinerte dokumentmodeller for domene-spesifikke skjemaer og dokumenttyper.",
          "subtopics": [
            "Custom model training",
            "Labeling strategies",
            "Training data preparation",
            "Model evaluation",
            "Model versioning"
          ]
        },
        {
          "id": "speech-services-speech-to-text",
          "title": "Speech Services - Speech-to-Text and Real-time Transcription",
          "description": "Real-tids taletranskripsjon, batch-transkripsjon, og støjredusjonsalternativer for ulike inngangskilder.",
          "subtopics": [
            "Real-time transcription",
            "Batch transcription",
            "Noise reduction",
            "Accuracy optimization",
            "Multiple language support"
          ]
        },
        {
          "id": "speech-services-text-to-speech",
          "title": "Speech Services - Text-to-Speech and Neural Voices",
          "description": "Tekst til tale med naturlige neural-stemmer, prosodi-kontroll og multi-språk støtte.",
          "subtopics": [
            "Neural voices",
            "Prosody control",
            "SSML markup",
            "Voice customization",
            "Audio output formats"
          ]
        },
        {
          "id": "speech-services-speaker-recognition",
          "title": "Speech Services - Speaker Recognition and Identification",
          "description": "Talergjengjøring, taleverifikasjon og identifisering av talere for autentiserings- og sikkerhetssituasjoner.",
          "subtopics": [
            "Speaker verification",
            "Speaker identification",
            "Voice profiles",
            "Enrollment process",
            "Security considerations"
          ]
        },
        {
          "id": "language-services-text-analytics",
          "title": "Language Services - Text Analytics for Sentiment and Key Phrases",
          "description": "Sentimentanalyse, nøkkelfraseekstraksjon, språkgjenkjenning og tekstklassifisering for norsk og andre språk.",
          "subtopics": [
            "Sentiment analysis",
            "Key phrase extraction",
            "Language detection",
            "Named entity recognition",
            "Text classification"
          ]
        },
        {
          "id": "language-services-question-answering",
          "title": "Language Services - Question Answering and Knowledge Mining",
          "description": "Bygging av kunnskapsressurser som svarer på spørsmål basert på strukturert og ustrukturert innhold.",
          "subtopics": [
            "Knowledge base creation",
            "Source document integration",
            "Question-answer pairs",
            "Multi-turn conversations",
            "Metadata filtering"
          ]
        },
        {
          "id": "language-services-custom-text-classification",
          "title": "Language Services - Custom Text Classification and NER",
          "description": "Egendefinert tekstklassifisering og navngitt enhetsgjengjøring for domene-spesifikke dokumenter.",
          "subtopics": [
            "Custom classification",
            "Named entity recognition",
            "Training data preparation",
            "Model evaluation",
            "Batch processing"
          ]
        },
        {
          "id": "translator-document-translation",
          "title": "Translator Service - Document Translation and Multi-language Support",
          "description": "Oversetting av hele dokumenter mens format og struktur bevares, med støtte for 100+ språk.",
          "subtopics": [
            "Document translation",
            "Format preservation",
            "Batch translation",
            "Language detection",
            "Quality estimation"
          ]
        },
        {
          "id": "translator-custom-neural-models",
          "title": "Translator Service - Custom Neural Translation Models",
          "description": "Trening av egendefinerte oversettelsesmodeller for domene-spesifikk eller terminologi-preget innhold.",
          "subtopics": [
            "Custom model training",
            "Terminology handling",
            "Domain adaptation",
            "Model evaluation",
            "Parallel corpus preparation"
          ]
        },
        {
          "id": "content-understanding-multimodal-analysis",
          "title": "Content Understanding - Multimodal Analysis and Video Intelligence",
          "description": "Analyse av videoer, kombinering av visuell og tekstlig informasjon, samt ekstraksjon av insights fra multimodalt innhold.",
          "subtopics": [
            "Video indexing",
            "Scene detection",
            "Visual-semantic fusion",
            "Motion detection",
            "Event detection"
          ]
        },
        {
          "id": "ai-services-networking-security",
          "title": "Azure AI Services - Networking, Security and Private Endpoints",
          "description": "Nettverkskonfigurering, private endpoints, VNet-integrering og sikkerhetstiltak for Azure AI Services.",
          "subtopics": [
            "Private endpoints",
            "Virtual network integration",
            "Managed identity",
            "API authentication",
            "Data encryption"
          ]
        },
        {
          "id": "ai-services-monitoring-logging",
          "title": "Azure AI Services - Monitoring, Logging and Diagnostics",
          "description": "Overvåking av Azure AI Services med Application Insights, diagnostikklogging og kostnadsanalyse.",
          "subtopics": [
            "Application Insights integration",
            "Diagnostic logging",
            "Metrics and alerts",
            "Cost tracking",
            "Performance analysis"
          ]
        },
        {
          "id": "ai-services-api-best-practices",
          "title": "Azure AI Services - API Design and Best Practices",
          "description": "Beste praksis for bruk av Azure AI Services API-er, feilhåndtering, retry-logikk og rate limiting.",
          "subtopics": [
            "Error handling patterns",
            "Retry strategies",
            "Rate limiting",
            "Batching requests",
            "API versioning"
          ]
        },
        {
          "id": "ai-services-governance-compliance",
          "title": "Azure AI Services - Data Governance and Compliance",
          "description": "Datastyringsrammer, samsvar med GDPR/datavern, og håndtering av sensitive data i Azure AI Services.",
          "subtopics": [
            "Data retention policies",
            "GDPR compliance",
            "Data residency",
            "Audit logging",
            "Consent management"
          ]
        },
        {
          "id": "ai-services-cost-optimization",
          "title": "Azure AI Services - Pricing Models and Cost Optimization",
          "description": "Prismodeller, kostnadsestimering, reserverte kapasiteter og strategier for kostnadsoptimalisering.",
          "subtopics": [
            "Pricing tiers",
            "Reserved capacity",
            "Cost estimation",
            "Usage patterns",
            "Budget management"
          ]
        },
        {
          "id": "ai-services-enterprise-architecture",
          "title": "Azure AI Services - Enterprise Architecture Patterns",
          "description": "Enterprise-arkitektmønstre for å integrere Azure AI Services i storskala løsninger med høy tilgjengelighet.",
          "subtopics": [
            "High availability patterns",
            "Disaster recovery",
            "Multi-region deployment",
            "Load balancing",
            "Service orchestration"
          ]
        },
        {
          "id": "ai-services-vs-foundry-tools-selection",
          "title": "Azure AI Services vs Foundry Tools - Platform Selection Guide",
          "description": "Veiledning for valg mellom individuelle AI Services, Azure AI Foundry, og andre plattformer basert på brukstilfeller.",
          "subtopics": [
            "Service comparison matrix",
            "Selection criteria",
            "Migration paths",
            "Use case mapping",
            "Cost-benefit analysis"
          ]
        }
      ]
    },
    "responsible-ai": {
      "name": "Responsible AI & Governance",
      "dir": "responsible-ai",
      "priority": "HIGH",
      "skills": [
        {
          "id": "responsible-ai-framework-overview",
          "title": "Responsible AI Framework - Microsoft's Core Principles",
          "description": "Oversikt over Microsofts Responsible AI-rammeverk med seks kjerneprinsipper (Fairness, Reliability, Safety, Privacy, Inclusiveness, Transparency) og implementering i praksis.",
          "subtopics": [
            "six-core-principles",
            "framework-structure",
            "implementation-roadmap",
            "organizational-alignment"
          ]
        },
        {
          "id": "ai-act-compliance-guide",
          "title": "AI Act Compliance - EU Regulation & Norwegian Implementation",
          "description": "Veileder for EU AI Act-compliance med fokus på risikobasert klassifisering, transparency-krav, og påvirkning for norsk offentlig sektor.",
          "subtopics": [
            "risk-based-classification",
            "transparency-requirements",
            "documentation-obligations",
            "public-sector-implications"
          ]
        },
        {
          "id": "bias-detection-mitigation-strategies",
          "title": "Bias Detection and Mitigation - Practical Approaches",
          "description": "Teknikker for å identifisere og redusere bias i AI-modeller, fra data-nivå til modell-output, med fokus på fairness-testing.",
          "subtopics": [
            "bias-sources-identification",
            "fairness-metrics",
            "dataset-debiasing",
            "model-testing-procedures"
          ]
        },
        {
          "id": "model-explainability-interpretability",
          "title": "Model Explainability and Interpretability - XAI Techniques",
          "description": "Metoder for å gjøre AI-modeller transparent og forklarbare, inkludert SHAP, LIME, og feature importance, essensielt for regulering og tillit.",
          "subtopics": [
            "explainability-methods",
            "interpretability-techniques",
            "feature-importance",
            "stakeholder-communication"
          ]
        },
        {
          "id": "ai-governance-structure-framework",
          "title": "AI Governance Structure - Building an Organizational Framework",
          "description": "Etablering av AI governance-struktur med roller, ansvar, oversight-mekanismer, og decision-making prosesser for enterprises.",
          "subtopics": [
            "governance-roles",
            "oversight-mechanisms",
            "decision-frameworks",
            "policy-development"
          ]
        },
        {
          "id": "ai-center-of-excellence-setup",
          "title": "AI Center of Excellence - Building Organizational Capability",
          "description": "Etablering og drift av AI CoE for å standardisere praksis, dele kunnskap, og sikre responsible AI implementering på tvers av organisasjonen.",
          "subtopics": [
            "coe-structure",
            "capability-building",
            "knowledge-sharing",
            "best-practice-standardization"
          ]
        },
        {
          "id": "red-teaming-ai-models",
          "title": "Red Teaming AI Models - Adversarial Testing & Security",
          "description": "Systematisk testing av AI-modeller for å identifisere svakheter, jailbreaks, og adversarial attacks før produksjon.",
          "subtopics": [
            "red-team-methodology",
            "adversarial-testing",
            "attack-vectors",
            "mitigation-strategies"
          ]
        },
        {
          "id": "content-safety-implementation",
          "title": "Content Safety and Harm Mitigation - Azure Implementation",
          "description": "Implementering av content-safety mekanismer for å forhindre harmful output, inkludert Azure Content Safety API og custom filtering.",
          "subtopics": [
            "content-filtering",
            "harm-categories",
            "azure-content-safety-api",
            "custom-safety-policies"
          ]
        },
        {
          "id": "ai-impact-assessment-framework",
          "title": "AI Impact Assessment - Evaluating Organizational and Societal Impact",
          "description": "Metodikk for å vurdere potensielle konsekvenser av AI-systemer på mennesker, organisasjoner og samfunn før implementering.",
          "subtopics": [
            "impact-dimensions",
            "assessment-methodology",
            "stakeholder-analysis",
            "mitigation-planning"
          ]
        },
        {
          "id": "transparency-documentation-standards",
          "title": "Transparency and Documentation - Regulatory and Best Practice Standards",
          "description": "Standard for transparensdokumentasjon, modellkort, datablad og impact statements for compliance og tillit.",
          "subtopics": [
            "model-cards",
            "data-sheets",
            "impact-statements",
            "documentation-templates"
          ]
        },
        {
          "id": "gdpr-compliance-ai-systems",
          "title": "GDPR Compliance for AI Systems - Data Privacy in Practice",
          "description": "Implementering av GDPR-krav i AI-løsninger, inkludert data retention, erasure rights, og persondata-håndtering.",
          "subtopics": [
            "personal-data-handling",
            "erasure-mechanisms",
            "data-retention-policies",
            "privacy-by-design"
          ]
        },
        {
          "id": "algorithmic-accountability-auditability",
          "title": "Algorithmic Accountability - Audit Trails and Traceability",
          "description": "Mekanismer for å spore AI-systemer, dokumentere beslutninger, og etablere ansvar for algoritmer i kritiske applikasjoner.",
          "subtopics": [
            "audit-trails",
            "decision-logging",
            "traceability-standards",
            "accountability-mechanisms"
          ]
        },
        {
          "id": "fairness-testing-measurement",
          "title": "Fairness Testing and Measurement - Quantifying Equity",
          "description": "Metoder for å måle og teste fairness i AI-modeller across demografiske grupper og bruksscenarier.",
          "subtopics": [
            "fairness-metrics",
            "demographic-parity",
            "equalized-odds",
            "testing-methodologies"
          ]
        },
        {
          "id": "ai-ethics-in-public-sector",
          "title": "AI Ethics in Public Sector - Norwegian Government Context",
          "description": "Etiske rammer spesifikt for bruk av AI i norsk offentlig administrasjon, inkludert lovkrav og nasjonale retningslinjer.",
          "subtopics": [
            "government-ai-guidelines",
            "public-sector-ethics",
            "citizen-trust",
            "norwegian-regulations"
          ]
        },
        {
          "id": "model-monitoring-drift-detection",
          "title": "Model Monitoring and Drift Detection - Ongoing Compliance",
          "description": "Overvåking av AI-modeller i produksjon for å detektere data drift, performance degradation, og bias-drift over tid.",
          "subtopics": [
            "drift-detection",
            "performance-monitoring",
            "bias-drift",
            "alerting-mechanisms"
          ]
        },
        {
          "id": "stakeholder-communication-ai-decisions",
          "title": "Stakeholder Communication - Explaining AI Decisions to Non-Technical Audiences",
          "description": "Strategier for å formidle AI-systemer, deres begrensninger og beslutninger til ikke-teknisk ledelse, brukere og publikum.",
          "subtopics": [
            "communication-strategies",
            "simplification-techniques",
            "visualization",
            "trust-building"
          ]
        },
        {
          "id": "ai-risk-taxonomy-classification",
          "title": "AI Risk Taxonomy - Classification and Risk Levels",
          "description": "Klassifiseringsrammeverk for å kategorisere AI-risker (høy, medium, lav) basert på konsekvenser og sannsyn for reguleringsmessig og organisatorisk styring.",
          "subtopics": [
            "risk-categories",
            "impact-assessment",
            "probability-estimation",
            "risk-matrices"
          ]
        },
        {
          "id": "responsible-ai-policy-development",
          "title": "Responsible AI Policy Development - Creating Organizational Standards",
          "description": "Prosess for å utvikle og implementere enterprise AI-policies som sikrer responsible praksis og compliance.",
          "subtopics": [
            "policy-framework",
            "stakeholder-engagement",
            "implementation-roadmap",
            "enforcement-mechanisms"
          ]
        },
        {
          "id": "data-quality-responsible-ai",
          "title": "Data Quality for Responsible AI - Ensuring Training Data Integrity",
          "description": "Best practices for datasett-kvalitet, dokumentasjon, og bias-mitigering for å sikre rettferdige og pålitelige AI-modeller.",
          "subtopics": [
            "dataset-documentation",
            "quality-standards",
            "bias-sources",
            "cleaning-strategies"
          ]
        },
        {
          "id": "human-in-the-loop-oversight",
          "title": "Human-in-the-Loop and Oversight - Maintaining Human Agency",
          "description": "Design og implementering av HITL-systemer som sikrer menneskelig oversikt over kritiske AI-beslutninger og vedlikeholder human agency.",
          "subtopics": [
            "hitl-design-patterns",
            "review-workflows",
            "escalation-procedures",
            "human-override"
          ]
        },
        {
          "id": "responsible-ai-training-awareness",
          "title": "Responsible AI Training and Awareness - Organizational Capability",
          "description": "Utvikling og gjennomføring av opplæring for å bygge ansvarsfull AI-kultur på tvers av organisasjonen.",
          "subtopics": [
            "training-curriculum",
            "awareness-campaigns",
            "role-specific-training",
            "certification-programs"
          ]
        },
        {
          "id": "continuous-improvement-feedback-loops",
          "title": "Continuous Improvement and Feedback Loops - Iterative Governance",
          "description": "Mekanismer for kontinuerlig forbedring av AI-systemer basert på feedback, incident-logs, og evolusjon av best practices.",
          "subtopics": [
            "feedback-mechanisms",
            "incident-review",
            "improvement-cycles",
            "lessons-learned"
          ]
        }
      ]
    },
    "copilot-extensibility": {
      "name": "Copilot Extensibility & Integration",
      "dir": "copilot-extensibility",
      "priority": "HIGH",
      "skills": [
        {
          "id": "declarative-agents-fundamentals",
          "title": "Declarative Agents - Design and Implementation",
          "description": "Grunnleggende prinsipper for deklarative agenter i Copilot Studio. Dekker agent-konfigurering, instruksjoner, grounding, og triggering mekanismer.",
          "subtopics": [
            "agent-definition-syntax",
            "instruction-design",
            "grounding-configuration",
            "trigger-patterns",
            "response-handling"
          ]
        },
        {
          "id": "custom-engine-agents-development",
          "title": "Custom Engine Agents - Advanced Configuration",
          "description": "Bygging og distribusjon av egendefinerte motorer i Copilot Studio. Dekker engine-arkitektur, API-integrasjon, og deployment-strategier.",
          "subtopics": [
            "engine-architecture",
            "api-integration-patterns",
            "authentication-methods",
            "error-handling",
            "scaling-considerations"
          ]
        },
        {
          "id": "copilot-studio-topics-and-entities",
          "title": "Topics and Entities in Copilot Studio",
          "description": "Semantisk organisering av samtaler ved hjelp av topics og custom entities. Fokus på kontekst-styring og dialogflyt.",
          "subtopics": [
            "topic-design-patterns",
            "entity-extraction",
            "entity-resolution",
            "context-management",
            "conversation-routing"
          ]
        },
        {
          "id": "microsoft-graph-api-copilot-integration",
          "title": "Microsoft Graph API for Copilot Extensions",
          "description": "Integrasjon med Graph API for å bygge kontekstverige Copilot-utvidelser. Dekker permission-modeller, data-tilgang, og use cases.",
          "subtopics": [
            "graph-api-permissions",
            "delegated-vs-application-auth",
            "common-entities-access",
            "change-notifications",
            "performance-optimization"
          ]
        },
        {
          "id": "copilot-connectors-design-patterns",
          "title": "Copilot Connectors - Implementation Patterns",
          "description": "Utvikling og bruk av connectors for å koble Copilot til eksterne systemer. Dekker connector-typer, autentisering, og best practices.",
          "subtopics": [
            "connector-architecture",
            "oauth-flow-setup",
            "webhook-integration",
            "error-resilience",
            "rate-limiting-strategies"
          ]
        },
        {
          "id": "mcp-protocol-copilot-studio",
          "title": "Model Context Protocol (MCP) in Copilot Studio",
          "description": "Bruk av MCP-protokollen for å standardisere agent-integrering. Dekker server-setup, client-implementering, og interoperabilitet.",
          "subtopics": [
            "mcp-architecture",
            "server-implementation",
            "client-integration",
            "resource-definitions",
            "tool-discovery"
          ]
        },
        {
          "id": "teams-copilot-message-extensions",
          "title": "Teams Copilot Message Extensions and Plugins",
          "description": "Utvidelse av Copilot i Teams gjennom message extensions og adaptive cards. Fokus på brukeropplevelse og integrering.",
          "subtopics": [
            "message-extension-setup",
            "adaptive-card-design",
            "action-handling",
            "search-integration",
            "notification-patterns"
          ]
        },
        {
          "id": "sharepoint-copilot-agents",
          "title": "SharePoint and OneDrive Copilot Agents",
          "description": "Bygging av kontekstverige agenter som arbeider med SharePoint og OneDrive-innhold. Dekker dokumenttilgang, søk, og personalisering.",
          "subtopics": [
            "content-search-integration",
            "document-context-extraction",
            "permission-inheritance",
            "version-handling",
            "metadata-enrichment"
          ]
        },
        {
          "id": "m365-copilot-plugins-ecosystem",
          "title": "M365 Copilot Plugins - Ecosystem and Distribution",
          "description": "Opprettelse, testing, og distribusjon av plugins til M365 Copilot-økosystemet. Dekker plugin-manifest, katalog, og applikasjonsgovernance.",
          "subtopics": [
            "plugin-manifest-schema",
            "plugin-discovery-catalog",
            "version-management",
            "compatibility-testing",
            "marketplace-submission"
          ]
        },
        {
          "id": "copilot-orchestration-multi-agent",
          "title": "Multi-Agent Orchestration in Copilot",
          "description": "Koordinering av flere agenter for komplekse arbeidsflyt. Dekker agent-samarbeid, statehåndtering, og resultat-aggregering.",
          "subtopics": [
            "agent-coordination-patterns",
            "state-management",
            "parallel-execution",
            "fallback-mechanisms",
            "result-merging"
          ]
        },
        {
          "id": "copilot-dlp-and-governance",
          "title": "Data Loss Prevention and Governance in Copilot",
          "description": "Implementering av DLP-retningslinjer og sikkerhetstiltak for Copilot-utvidelser. Dekker dataklassifisering, masking, og compliance.",
          "subtopics": [
            "dlp-policy-configuration",
            "sensitive-data-detection",
            "output-masking",
            "audit-logging",
            "compliance-frameworks"
          ]
        },
        {
          "id": "copilot-analytics-and-usage-insights",
          "title": "Copilot Analytics and Usage Monitoring",
          "description": "Overvåking og analyse av Copilot-bruk på tvers av organisasjonen. Dekker telemetri, dashboards, og actionable insights.",
          "subtopics": [
            "usage-metrics-collection",
            "dashboard-creation",
            "user-adoption-tracking",
            "performance-monitoring",
            "roi-measurement"
          ]
        },
        {
          "id": "copilot-prompt-engineering-governance",
          "title": "Prompt Engineering and Governance for Copilot",
          "description": "Best practices for prompt-design og organisatorisk styring av prompts. Dekker prompt-templates, validering, og versionering.",
          "subtopics": [
            "prompt-template-patterns",
            "instruction-consistency",
            "guardrail-implementation",
            "version-control",
            "a-b-testing"
          ]
        },
        {
          "id": "declarative-agents-grounding-strategies",
          "title": "Grounding Strategies for Declarative Agents",
          "description": "Teknikker for å grunde agenter i organisasjonsdata og system-kontekst. Dekker data-tilgang, kontekst-inneksjon, og relevans-scoring.",
          "subtopics": [
            "context-injection-patterns",
            "data-source-selection",
            "relevance-ranking",
            "freshness-management",
            "fallback-responses"
          ]
        },
        {
          "id": "copilot-studio-nlp-configuration",
          "title": "NLP Configuration and Intent Recognition",
          "description": "Konfigurering av natural language processing for bedre agent-forståelse. Dekker intent-gjenkjenning, entitetsmapping, og språkmodeller.",
          "subtopics": [
            "intent-classifier-training",
            "entity-model-tuning",
            "multi-language-support",
            "confidence-thresholds",
            "feedback-loops"
          ]
        },
        {
          "id": "copilot-extensibility-security-patterns",
          "title": "Security Patterns for Copilot Extensions",
          "description": "Implementering av sikkerhet i utvidelser: autentisering, autorisering, og datakryptering. Fokus på enterprise-krav.",
          "subtopics": [
            "token-management",
            "secret-rotation",
            "encryption-at-rest",
            "encryption-in-transit",
            "zero-trust-principles"
          ]
        },
        {
          "id": "power-automate-copilot-integration",
          "title": "Power Automate and Copilot Studio Integration",
          "description": "Kobling av Power Automate-flyter til Copilot for utvidet funksjonalitet. Dekker flow-triggering, parameter-passing, og resultat-håndtering.",
          "subtopics": [
            "flow-invocation-patterns",
            "connector-actions",
            "error-handling-flows",
            "approval-workflows",
            "notification-triggers"
          ]
        },
        {
          "id": "copilot-context-window-optimization",
          "title": "Context Window Optimization for Copilot",
          "description": "Optimalisering av kontekst-bruken for bedre agent-ytelse. Dekker window-størrelse, prioritering, og komprimering av informasjon.",
          "subtopics": [
            "window-size-tuning",
            "context-prioritization",
            "compression-techniques",
            "relevance-filtering",
            "token-budgeting"
          ]
        },
        {
          "id": "adaptive-cards-copilot-responses",
          "title": "Adaptive Cards for Rich Copilot Responses",
          "description": "Utvikling av rike responsformat ved hjelp av Adaptive Cards. Dekker design, interaktivitet, og cross-platform kompatibilitet.",
          "subtopics": [
            "card-schema-design",
            "interactive-elements",
            "conditional-rendering",
            "accessibility-standards",
            "theming-customization"
          ]
        },
        {
          "id": "copilot-api-rate-limiting-resilience",
          "title": "API Rate Limiting and Resilience Patterns",
          "description": "Håndtering av rate-limiting og bygning av resiliente agent-utvidelser. Dekker retry-strategier, caching, og graceful degradation.",
          "subtopics": [
            "rate-limit-headers",
            "exponential-backoff",
            "circuit-breaker-patterns",
            "local-caching",
            "degraded-mode-operation"
          ]
        },
        {
          "id": "copilot-studio-localization-globalization",
          "title": "Localization and Globalization in Copilot",
          "description": "Tilpassing av Copilot-agenter for flere språk og regionale kontekster. Dekker oversettelse, kulturelle nyanser, og compliance.",
          "subtopics": [
            "language-detection",
            "multi-language-responses",
            "cultural-adaptation",
            "regional-compliance",
            "character-encoding"
          ]
        },
        {
          "id": "enterprise-governance-copilot-deployment",
          "title": "Enterprise Governance and Deployment Controls",
          "description": "Organisatoriske kontroller for Copilot-distribusjon og -styring. Dekker rollebasert tilgang, godkjenningsprosesser, og compliance-rapportering.",
          "subtopics": [
            "rbac-configuration",
            "approval-workflows",
            "audit-trails",
            "policy-enforcement",
            "blast-radius-limiting"
          ]
        }
      ]
    },
    "prompt-engineering": {
      "name": "Prompt Engineering & LLM Optimization",
      "dir": "prompt-engineering",
      "priority": "HIGH",
      "skills": [
        {
          "id": "system-message-design-patterns",
          "title": "System Message Design Patterns and Best Practices",
          "description": "Strategi for å utforme effektive system prompts som styrer modelloppførsel, tone og kontekst. Dekker persona-design, instruksjonshieraki og constraint-setting.",
          "subtopics": [
            "persona-definition",
            "instruction-hierarchy",
            "constraint-setting",
            "role-specification",
            "context-framing"
          ]
        },
        {
          "id": "few-shot-learning-techniques",
          "title": "Few-Shot and Zero-Shot Learning Techniques",
          "description": "Teknikker for å demonstrere ønsket oppførsel gjennom eksempler eller instruksjoner uten eksempler. Fokus på eksempel-utvalg og formatering.",
          "subtopics": [
            "few-shot-examples",
            "zero-shot-prompting",
            "example-selection-strategy",
            "in-context-learning",
            "demonstration-quality"
          ]
        },
        {
          "id": "chain-of-thought-prompting",
          "title": "Chain-of-Thought and Reasoning Prompts",
          "description": "Fremme trinnvis resonnering gjennom prompts som eksplisitt ber modellen 'tenke høyt'. Dekker standard og avanserte CoT-varianter.",
          "subtopics": [
            "explicit-reasoning-steps",
            "intermediate-conclusions",
            "error-correction-loops",
            "tree-of-thought",
            "verification-steps"
          ]
        },
        {
          "id": "reasoning-models-o1-o3-optimization",
          "title": "Reasoning Models (O1/O3) Optimization and Usage",
          "description": "Spesialisert prompt-design for OpenAI O1/O3-modeller som har egen reasoning-fase. Inkluderer trade-offs mellom hastighet og nøyaktighet.",
          "subtopics": [
            "extended-thinking-configuration",
            "problem-decomposition",
            "reasoning-budget-allocation",
            "output-format-handling",
            "cost-performance-trade-offs"
          ]
        },
        {
          "id": "structured-output-formatting",
          "title": "Structured Output and JSON Mode",
          "description": "Sikre at modellen returnerer data i spesifikke formater (JSON, XML, CSV). Inkluderer constraint-enforcement og validering.",
          "subtopics": [
            "json-schema-specification",
            "format-enforcement",
            "parsing-strategies",
            "error-recovery",
            "schema-validation"
          ]
        },
        {
          "id": "function-calling-and-tool-use",
          "title": "Function Calling and Tool Use Patterns",
          "description": "Design av function calls og tool-integration i prompts. Hvordan strukturere tool-descriptions og handle tool-responses.",
          "subtopics": [
            "tool-description-design",
            "parameter-specification",
            "error-handling-in-tools",
            "sequential-tool-calls",
            "fallback-strategies"
          ]
        },
        {
          "id": "grounding-and-knowledge-injection",
          "title": "Grounding and Knowledge Injection Techniques",
          "description": "Inkorporering av kontekstspesifikk kunnskap i prompts for å redusere hallucineringer. Dekker document-grounding og RAG-integration.",
          "subtopics": [
            "document-context-injection",
            "fact-verification",
            "citation-guidance",
            "knowledge-cutoff-handling",
            "external-data-integration"
          ]
        },
        {
          "id": "temperature-sampling-and-parameters",
          "title": "Temperature, Sampling, and Generation Parameters",
          "description": "Tuning av modellparametere som temperature, top-p, frequency penalty. Påvirkning på kreativitet vs konsistens.",
          "subtopics": [
            "temperature-calibration",
            "top-k-top-p-sampling",
            "frequency-penalties",
            "presence-penalties",
            "output-length-control"
          ]
        },
        {
          "id": "token-optimization-and-efficiency",
          "title": "Token Optimization and Cost Efficiency",
          "description": "Strategier for å redusere token-bruk uten tap av kvalitet. Cache-strategier, prompt-compression og effektiv kontekst-bruk.",
          "subtopics": [
            "prompt-compression",
            "context-prioritization",
            "token-counting-strategies",
            "caching-patterns",
            "batch-processing-efficiency"
          ]
        },
        {
          "id": "prompt-testing-and-evaluation",
          "title": "Prompt Testing, Evaluation and Iteration",
          "description": "Metodikk for å teste, måle og iterere på prompts. Metrikkker, benchmarks og A/B-testing av prompt-varianter.",
          "subtopics": [
            "quality-metrics",
            "benchmark-datasets",
            "ab-testing-prompts",
            "regression-detection",
            "iteration-frameworks"
          ]
        },
        {
          "id": "adversarial-prompting-and-jailbreaks",
          "title": "Adversarial Prompting and Security Testing",
          "description": "Identifikasjon og mitigering av adversarial prompts og jailbreak-teknikker. Prompt-injection-beskyttelse.",
          "subtopics": [
            "prompt-injection-patterns",
            "jailbreak-techniques",
            "defense-mechanisms",
            "input-sanitization",
            "safety-guardrails"
          ]
        },
        {
          "id": "multi-turn-conversation-management",
          "title": "Multi-Turn Conversation and Context Management",
          "description": "Håndtering av langvarige samtaler med memory, context-window management og konsistensmaintenance.",
          "subtopics": [
            "conversation-history-management",
            "context-window-limits",
            "summary-strategies",
            "state-tracking",
            "consistency-preservation"
          ]
        },
        {
          "id": "role-playing-and-persona-techniques",
          "title": "Role-Playing and Persona-Based Prompting",
          "description": "Bruk av roller og personas for å styre modelloppførsel. Expert-personas, character-development og tone-control.",
          "subtopics": [
            "expert-persona-design",
            "character-consistency",
            "tone-and-style-guidance",
            "perspective-shifting",
            "behavioral-constraints"
          ]
        },
        {
          "id": "error-handling-and-fallback-prompting",
          "title": "Error Handling and Fallback Prompting Strategies",
          "description": "Design av prompts som gracefully håndterer edge cases, usikkerhet og error-conditions. Fallback-strategier og error-recovery.",
          "subtopics": [
            "uncertainty-expression",
            "confidence-scoring",
            "fallback-strategies",
            "error-detection-patterns",
            "graceful-degradation"
          ]
        },
        {
          "id": "domain-specific-prompt-optimization",
          "title": "Domain-Specific Prompt Optimization",
          "description": "Tilpassing av prompts til spesifikke domener (juridisk, medisinsk, teknisk). Domene-termer, kontekst og best practices.",
          "subtopics": [
            "legal-domain-prompting",
            "medical-domain-prompting",
            "technical-domain-prompting",
            "industry-terminology",
            "regulatory-compliance"
          ]
        },
        {
          "id": "multimodal-prompt-design",
          "title": "Multimodal Prompt Design with Images and Text",
          "description": "Prompting-strategier for multimodale modeller. Kombinasjon av tekst, bilder, og annen kontekst. Vision-grounding.",
          "subtopics": [
            "image-annotation-prompts",
            "vision-instruction-design",
            "cross-modal-understanding",
            "image-quality-requirements",
            "layout-interpretation"
          ]
        },
        {
          "id": "real-time-reasoning-performance",
          "title": "Real-Time Reasoning and Performance Optimization",
          "description": "Prompting-teknikker for response-latency-kritiske applikasjoner. Streaming, partial-responses og progressive-generation.",
          "subtopics": [
            "streaming-optimization",
            "partial-response-handling",
            "progressive-generation",
            "latency-constraints",
            "bandwidth-optimization"
          ]
        },
        {
          "id": "regulatory-and-compliance-prompting",
          "title": "Regulatory and Compliance-Aware Prompting",
          "description": "Prompts designet for å møte regulatoriske krav (GDPR, AI Act, sektor-spesifikke). Compliance-output og audit-trails.",
          "subtopics": [
            "gdpr-compliance-prompts",
            "ai-act-alignment",
            "data-minimization",
            "audit-trail-requirements",
            "consent-handling"
          ]
        }
      ]
    },
    "cost-optimization": {
      "name": "Cost Optimization & FinOps for AI",
      "dir": "cost-optimization",
      "priority": "HIGH",
      "skills": [
        {
          "id": "token-counting-optimization",
          "title": "Token Counting and Optimization Strategies",
          "description": "Teknikker for å telle og optimalisere tokenforbruk i Azure OpenAI og Copilot Studio. Dekker token-estimering, kompresjon og strategier for å redusere kostnader per forespørsel.",
          "subtopics": [
            "token-estimation-methods",
            "prompt-compression",
            "response-length-limits",
            "token-budgeting"
          ]
        },
        {
          "id": "semantic-caching-patterns",
          "title": "Semantic Caching for AI Workloads",
          "description": "Implementering av intelligente cache-strategier som lagrer semantisk like resultater for å unngå gjentatt API-kall. Reduserer latency og kostnader på repetitive oppgaver.",
          "subtopics": [
            "embedding-based-caching",
            "cache-invalidation",
            "similarity-thresholds",
            "cache-storage-options"
          ]
        },
        {
          "id": "reserved-capacity-planning",
          "title": "Reserved Capacity and Commitment Discounts",
          "description": "Planlegging av reserved capacity for Azure AI Services og Azure OpenAI med fokus på rabatter og kostnadsforutsigbarhet. Sammenligning av pay-as-you-go versus kommitment-modeller.",
          "subtopics": [
            "commitment-tiers",
            "reservation-sizing",
            "workload-forecasting",
            "discount-optimization"
          ]
        },
        {
          "id": "model-selection-price-performance",
          "title": "Model Selection for Cost-Efficiency",
          "description": "Veileder for å velge riktig modell basert på pris, ytelse og latency-krav. Dekker når man skal bruke mindre modeller som GPT-4o Mini eller Phi-4 versus større modeller.",
          "subtopics": [
            "model-pricing-comparison",
            "performance-benchmarks",
            "latency-requirements",
            "task-model-fit"
          ]
        },
        {
          "id": "ptu-vs-paygo-economics",
          "title": "PTU vs Pay-as-You-Go: Economic Decision Framework",
          "description": "Detaljert analyse av når man skal bruke Provisioned Throughput Units (PTU) versus pay-as-you-go for Azure OpenAI. Breakeven-analyse og kapasitetsplanlegging.",
          "subtopics": [
            "ptu-pricing-model",
            "paygo-cost-calculation",
            "breakeven-analysis",
            "capacity-planning"
          ]
        },
        {
          "id": "batch-processing-cost-reduction",
          "title": "Batch Processing APIs for Non-Latency-Critical Workloads",
          "description": "Bruk av Azure OpenAI Batch API for å oppnå 50% rabatt på ikke-kritiske workloads. Optimalisering av batchstørrelser og scheduling.",
          "subtopics": [
            "batch-api-setup",
            "job-scheduling",
            "cost-savings-calculation",
            "latency-tradeoffs"
          ]
        },
        {
          "id": "azure-cost-management-ai",
          "title": "Azure Cost Management and Budget Monitoring for AI",
          "description": "Oppsett av budsjettovervåking, kostnadsalertinger og forecast-modeller spesifikt for AI og Copilot-ressurser i Azure.",
          "subtopics": [
            "cost-alerts",
            "budgeting-governance",
            "forecast-models",
            "anomaly-detection"
          ]
        },
        {
          "id": "request-batching-aggregation",
          "title": "Request Batching and Response Aggregation",
          "description": "Teknikker for å kombinere flere små forespørsler til færre store forespørsler for bedre token-effektivitet. Dekker payload-design og respons-parsing.",
          "subtopics": [
            "payload-consolidation",
            "response-unpacking",
            "latency-impact",
            "implementation-patterns"
          ]
        },
        {
          "id": "prompt-engineering-cost-reduction",
          "title": "Prompt Engineering for Cost Reduction",
          "description": "Bruk av effektive prompt-teknikker som few-shot learning og chain-of-thought som reduserer behov for lange system-prompts eller flere API-kall.",
          "subtopics": [
            "few-shot-efficiency",
            "chain-of-thought-optimization",
            "system-prompt-length",
            "instruction-clarity"
          ]
        },
        {
          "id": "vector-storage-cost-optimization",
          "title": "Vector Storage and Embedding Cost Optimization",
          "description": "Optimalisering av embedding-kostnader og vektorlagring for RAG-systemer. Dekker modellvalg, dimensionalitetsreduksjon og lagring av embedding-vektorer.",
          "subtopics": [
            "embedding-model-selection",
            "dimension-reduction",
            "vector-db-costs",
            "storage-optimization"
          ]
        },
        {
          "id": "ai-builder-credits-transition",
          "title": "AI Builder and Power Platform Credits Strategy",
          "description": "Kostnadsstrategi for å migrere fra AI Builder-kreditter til Azure AI Services. Dekker licensing-modeller og når det er lønnsomt å bytte.",
          "subtopics": [
            "ai-builder-credit-model",
            "azure-licensing-costs",
            "migration-economics",
            "platform-selection"
          ]
        },
        {
          "id": "cost-allocation-chargeback",
          "title": "Cost Allocation and Chargeback Models",
          "description": "Implementering av chargeback-modeller for AI-tjenester i organisasjoner. Dekker kostnadsfordeling på teams, prosjekter og avdelinger.",
          "subtopics": [
            "tagging-strategies",
            "cost-center-allocation",
            "showback-models",
            "governance-controls"
          ]
        },
        {
          "id": "budget-forecasting-ai-projects",
          "title": "Budget Forecasting and Financial Planning for AI",
          "description": "Teknikker for å prognostisere AI-kostnader basert på forventet vekst, brukermønstre og modellvalg. Inkluderer scenarioplanlegging.",
          "subtopics": [
            "usage-forecasting",
            "growth-projections",
            "scenario-analysis",
            "financial-modeling"
          ]
        },
        {
          "id": "small-language-models-economics",
          "title": "Small Language Models: Economics and Use Cases",
          "description": "Analyse av når små modeller (Phi, Llama, GPT-4o Mini) gir beste kostnad-nytte-forhold. On-premises versus cloud-hosting kostnader.",
          "subtopics": [
            "small-model-pricing",
            "on-premises-hosting",
            "inference-costs",
            "accuracy-tradeoffs"
          ]
        },
        {
          "id": "rag-query-cost-reduction",
          "title": "RAG Query Cost Optimization",
          "description": "Reduksjon av kostnader i RAG-pipelines gjennom intelligente retrieval-strategier, query-rewriting og resultat-caching.",
          "subtopics": [
            "retrieval-optimization",
            "reranking-cost",
            "query-rewriting",
            "cache-hit-rates"
          ]
        },
        {
          "id": "azure-ai-foundry-cost-governance",
          "title": "Azure AI Foundry Cost Governance and Controls",
          "description": "Oppsett av kostnads-governance, quotas og limits innenfor Azure AI Foundry for å forhindre utgiftsoverskridelser.",
          "subtopics": [
            "quota-management",
            "rate-limiting",
            "spending-caps",
            "usage-monitoring"
          ]
        },
        {
          "id": "multi-model-strategy-costs",
          "title": "Multi-Model Strategy: Cost-Performance Trade-offs",
          "description": "Strategi for å bruke flere modeller (GPT-4, GPT-4o Mini, specialized models) i samme løsning for optimal kostnadseffektivitet.",
          "subtopics": [
            "model-routing",
            "tiered-inference",
            "task-specific-models",
            "fallback-strategies"
          ]
        },
        {
          "id": "inference-endpoint-cost-optimization",
          "title": "Managed Inference Endpoints: Cost Optimization",
          "description": "Optimalisering av kostnader ved bruk av Azure AI Foundry managed endpoints. Autoscaling, batching og instance-sizing.",
          "subtopics": [
            "autoscaling-configuration",
            "instance-sizing",
            "idle-capacity",
            "endpoint-consolidation"
          ]
        },
        {
          "id": "licensing-compliance-cost-avoidance",
          "title": "Licensing Compliance and Cost Avoidance",
          "description": "Sikring av riktig licensering for Azure AI Services og Power Platform for å unngå overbetaling eller brudd på licensing-avtaler.",
          "subtopics": [
            "license-audit",
            "compliance-requirements",
            "optimization-opportunities",
            "enterprise-agreements"
          ]
        },
        {
          "id": "observability-cost-reduction",
          "title": "Observability and Monitoring Cost Optimization",
          "description": "Optimalisering av logging og monitoring-kostnader for AI-workloads. Sampling, aggregering og retention-policies.",
          "subtopics": [
            "log-sampling",
            "metric-aggregation",
            "retention-policies",
            "alert-optimization"
          ]
        }
      ]
    },
    "mlops-genaiops": {
      "name": "MLOps & GenAIOps",
      "dir": "mlops-genaiops",
      "priority": "HIGH",
      "skills": [
        {
          "id": "mlops-fundamentals-overview",
          "title": "MLOps Fundamentals - Lifecycle and Principles",
          "description": "Introduksjon til MLOps som disiplin, forskjell fra DevOps, og de kritiske fasene i ML-produktlivssyklusen fra data til monitoring.",
          "subtopics": [
            "ML lifecycle stages",
            "DevOps vs MLOps",
            "Team roles and responsibilities",
            "Governance frameworks"
          ]
        },
        {
          "id": "azure-ml-pipelines-orchestration",
          "title": "Azure ML Pipelines - Orchestration and Automation",
          "description": "Oppbygging av repeterbare ML-pipelines i Azure ML, automatisering av data-, trening- og inferenssteg, og best practices for produksjonspipelines.",
          "subtopics": [
            "Pipeline components",
            "Scheduled runs",
            "Trigger-based workflows",
            "Pipeline dependencies and monitoring"
          ]
        },
        {
          "id": "model-versioning-registry-management",
          "title": "Model Versioning and Registry Management",
          "description": "Versjonshåndtering av ML-modeller, asset tracking, reproducibility, og modellregisteret som sentral ressurs for governance.",
          "subtopics": [
            "Model registry structure",
            "Versioning strategies",
            "Metadata and provenance",
            "Model lineage tracking"
          ]
        },
        {
          "id": "ci-cd-for-ml-models",
          "title": "CI/CD Pipelines for Machine Learning Models",
          "description": "Implementering av continuous integration og deployment for ML, automatisering av testing, validering og utrulling av modeller.",
          "subtopics": [
            "Automated testing frameworks",
            "Model validation gates",
            "Canary deployments",
            "Rollback strategies"
          ]
        },
        {
          "id": "model-evaluation-frameworks",
          "title": "Model Evaluation Frameworks and Metrics",
          "description": "Systematisk evaluering av modellytelse, valg av riktige metrikker, A/B-testing og offline evaluering før produksjonsutrulling.",
          "subtopics": [
            "Classification and regression metrics",
            "Business-relevant metrics",
            "Offline evaluation",
            "Statistical significance testing"
          ]
        },
        {
          "id": "ab-testing-llm-applications",
          "title": "A/B Testing and Experimentation for AI Models",
          "description": "Design og utføring av eksperimenter for LLM-er og AI-modeller, metodologi for prompts, modeller og inferensparametere.",
          "subtopics": [
            "Experiment design",
            "Statistical power analysis",
            "Sample size calculation",
            "Multi-armed bandit strategies"
          ]
        },
        {
          "id": "data-drift-monitoring-detection",
          "title": "Data Drift Monitoring and Detection",
          "description": "Overvåking av input-datafordelinger over tid, deteksjon av drift som kan påvirke modellytelse, og triggering av retraining.",
          "subtopics": [
            "Distribution shift detection",
            "Statistical tests for drift",
            "Drift visualization",
            "Drift alerting thresholds"
          ]
        },
        {
          "id": "model-drift-performance-degradation",
          "title": "Model Drift and Performance Degradation Detection",
          "description": "Overvåking av modellytelsesforringelse i produksjon, årsaksanalyse og tiltak når prediksjoner blir unøyaktige.",
          "subtopics": [
            "Performance metric tracking",
            "Root cause analysis",
            "Retraining triggers",
            "Model performance dashboards"
          ]
        },
        {
          "id": "automated-retraining-pipelines",
          "title": "Automated Retraining Pipelines and Scheduling",
          "description": "Automatsering av modellretrening basert på data eller ytelsestrigger, planlegging av retrening, og sikring av modellkonsistens.",
          "subtopics": [
            "Retraining schedules",
            "Drift-triggered retraining",
            "Data collection for retraining",
            "Retraining validation"
          ]
        },
        {
          "id": "prompt-flow-production-deployment",
          "title": "Prompt Flow and Production Deployment",
          "description": "Bruk av Azure Prompt Flow for å bygge, teste og distribuere komplekse LLM-applikasjoner med tracking og versjonering.",
          "subtopics": [
            "Flow design patterns",
            "Prompt versioning in Prompt Flow",
            "Integration with pipelines",
            "Flow monitoring and debugging"
          ]
        },
        {
          "id": "model-deployment-strategies-azure",
          "title": "Model Deployment Strategies on Azure",
          "description": "Ulike strategier for modelldeployment: batch, real-time endpoints, serverless, og hybrid, samt kostnads- og ytelsehensyn.",
          "subtopics": [
            "Real-time vs batch inference",
            "Endpoint scaling",
            "Managed online endpoints",
            "Deployment environments"
          ]
        },
        {
          "id": "inferencing-optimization-caching",
          "title": "Inferencing Optimization and Caching",
          "description": "Optimalisering av inferensstynger, response time reduction, caching-strategier og kostnadseffektiv serving av modeller.",
          "subtopics": [
            "Inference latency optimization",
            "Model caching",
            "Batch inference",
            "Edge deployment options"
          ]
        },
        {
          "id": "llm-evaluation-production",
          "title": "LLM Evaluation in Production Contexts",
          "description": "Evaluering av LLM-outputs i produksjon inkludert kvalitet, relevans, hallusinasjoner, og bruk av reference-baserte og reference-frie metrikker.",
          "subtopics": [
            "Hallucination detection",
            "Output quality metrics",
            "Reference-based evaluation",
            "Human-in-the-loop evaluation"
          ]
        },
        {
          "id": "monitoring-observability-ml-systems",
          "title": "Monitoring and Observability for ML Systems",
          "description": "Helhettig overvåking av ML-systemer: data, modell, infrastruktur og business metrics, samt logging og alerting.",
          "subtopics": [
            "Metrics collection",
            "Logging strategies",
            "Alerting and SLOs",
            "Observability dashboards"
          ]
        },
        {
          "id": "governance-audit-ml-operations",
          "title": "Governance and Audit Trails in MLOps",
          "description": "Implementering av governance, compliance logging, audit trails, og dokumentasjon av alle MLOps-aktiviteter for compliance og transparens.",
          "subtopics": [
            "Audit logging",
            "Change tracking",
            "Model approval workflows",
            "Compliance documentation"
          ]
        },
        {
          "id": "genaiops-llm-specific-practices",
          "title": "GenAIOps - LLM-Specific MLOps Practices",
          "description": "MLOps tilpasset generative AI og LLM-er: prompt management, version control for prompts, og spesialiserte evaluerings- og deployment-strategier.",
          "subtopics": [
            "Prompt versioning and governance",
            "LLM-specific metrics",
            "Token cost optimization",
            "RAG pipeline orchestration"
          ]
        },
        {
          "id": "cost-optimization-mlops-pipelines",
          "title": "Cost Optimization in MLOps Pipelines",
          "description": "Reduksjon av compute-, data- og inferenskostnader i MLOps, ressursallokering, og kostnadsovervåking per modell og pipeline.",
          "subtopics": [
            "Compute resource optimization",
            "Storage cost reduction",
            "Inference cost tracking",
            "Budget allocation per project"
          ]
        },
        {
          "id": "infrastructure-as-code-mlops",
          "title": "Infrastructure as Code for MLOps",
          "description": "Bruk av IaC-verktøy for å definere og administrere MLOps-infrastruktur, inkludert pipelines, endpoints og monitoring som code.",
          "subtopics": [
            "Terraform for ML infrastructure",
            "Bicep templates",
            "Configuration management",
            "Environment reproducibility"
          ]
        },
        {
          "id": "mlops-security-access-control",
          "title": "Security and Access Control in MLOps",
          "description": "Sikkerhet i MLOps: autentisering, autorisasjon, secret management, og sikring av modeller og data i pipelines.",
          "subtopics": [
            "RBAC for ML resources",
            "Secret management",
            "Network security",
            "Data encryption in transit and at rest"
          ]
        },
        {
          "id": "feedback-loops-continuous-improvement",
          "title": "Feedback Loops and Continuous Improvement",
          "description": "Etablering av tilbakemeldingsmekanismer fra produksjon, bruk av brukerdata for modellforbetering, og iterativ optimalisering.",
          "subtopics": [
            "User feedback collection",
            "Production data labeling",
            "Active learning",
            "Improvement measurement"
          ]
        },
        {
          "id": "responsible-ai-mlops-integration",
          "title": "Responsible AI Integration in MLOps",
          "description": "Inkorporering av ansvarlig AI-praksis i MLOps: bias-testing, fairness-monitorering, transparens-dokumentasjon i hele lifecycle.",
          "subtopics": [
            "Bias detection in pipelines",
            "Fairness metrics tracking",
            "Model documentation",
            "Compliance with AI regulations"
          ]
        },
        {
          "id": "mlops-teams-collaboration-tools",
          "title": "MLOps Team Collaboration and Tools Integration",
          "description": "Samarbeidsverktøy for MLOps-team, integrasjon med M365 og Git, kommunikasjon om modellendringer og pipeline-status.",
          "subtopics": [
            "Git integration for ML",
            "Teams notifications",
            "Collaboration workflows",
            "Knowledge sharing practices"
          ]
        }
      ]
    },
    "norwegian-public-sector-governance": {
      "name": "Norwegian Public Sector AI Governance",
      "dir": "norwegian-public-sector-governance",
      "priority": "HIGH",
      "skills": [
        {
          "id": "utredningsinstruksen-ai-methodology",
          "title": "Utredningsinstruksen - AI Project Scoping and Methodology",
          "description": "Regjeringens utredningsinstruks anvendt på AI-prosjekter i offentlig sektor. Strukturerer analyse av behov, alternativer, kostnader og gevinster før implementering.",
          "subtopics": [
            "alternative-analysis",
            "cost-benefit-analysis",
            "stakeholder-involvement",
            "implementation-roadmap"
          ]
        },
        {
          "id": "forvaltningsloven-ai-decisions",
          "title": "Forvaltningsloven - AI Decision-Making and Public Administration",
          "description": "Lovkrav for enkeltvedtak og forvaltningsprovedyrer når AI brukes i offentlig beslutningstaking. Transparens, begrunnelse og klageadgang.",
          "subtopics": [
            "individual-decisions-requirements",
            "procedural-fairness",
            "documentation-and-reasoning",
            "appeals-process"
          ]
        },
        {
          "id": "digdir-principle-1-user-centric-design",
          "title": "Digdir Architecture Principles 1 - User-Centric AI Design",
          "description": "Digdirs første arkitekturprinsipp: brukerfokus som grunnlag for AI-løsninger. Inkluderer universell utforming og aksessibilitet.",
          "subtopics": [
            "user-research-requirements",
            "accessibility-wcag-compliance",
            "inclusive-design",
            "usability-testing"
          ]
        },
        {
          "id": "digdir-principle-2-interoperability",
          "title": "Digdir Architecture Principles 2 - Interoperability and Data Sharing",
          "description": "Digdirs andre prinsipp: semantisk og teknisk samhandling mellom AI-systemer og offentlige løsninger. Standarder og API-design.",
          "subtopics": [
            "api-standardization",
            "data-exchange-formats",
            "system-integration",
            "semantic-interoperability"
          ]
        },
        {
          "id": "digdir-principle-4-trust-security",
          "title": "Digdir Architecture Principles 4 - Trust and Security in AI",
          "description": "Digdirs fjerde prinsipp: sikkerhet, autentisitet og integritet. Kryptering, logging, tilgangskontroll og sikkerhetsprinsipper for AI.",
          "subtopics": [
            "encryption-standards",
            "audit-logging-requirements",
            "access-control-models",
            "security-architecture"
          ]
        },
        {
          "id": "digital-samhandling-eif-5-layers",
          "title": "European Interoperability Framework (EIF) - 5 Layers for AI Integration",
          "description": "Digdirs rammeverk for digital samhandling basert på EIF. Teknisk, semantisk, organisatorisk og juridisk samhandlingsmodell for AI i offentlig sektor.",
          "subtopics": [
            "technical-interoperability",
            "semantic-interoperability",
            "organizational-alignment",
            "legal-framework"
          ]
        },
        {
          "id": "dpia-norwegian-methodology-ai",
          "title": "Data Protection Impact Assessment (DPIA) - Norwegian AI Methodology",
          "description": "Gjennomføring av DPIA for AI-systemer etter personopplysningsloven. Risikokartlegging, behandlingsprinsipper og avbøtende tiltak.",
          "subtopics": [
            "privacy-by-design",
            "high-risk-assessment",
            "mitigation-strategies",
            "documentation-requirements"
          ]
        },
        {
          "id": "ros-analyse-ai-systems",
          "title": "ROS Analysis - Risk and Vulnerability Assessment for AI Systems",
          "description": "Risikoanalyse og sårbarhetsanalyse (ROS) tilpasset AI-løsninger i offentlig sektor. Direktoratet for samfunnssikkerhet og beredskaps metodikk.",
          "subtopics": [
            "risk-identification",
            "likelihood-impact-scoring",
            "vulnerability-categories",
            "remediation-planning"
          ]
        },
        {
          "id": "nsm-grunnprinsipper-ai-mapping",
          "title": "NSM Grunnprinsipper - Mapping to AI Security Architecture",
          "description": "Nasjonal sikkerhetsmyndighetens grunnprinsipper for informasjonssikkerhet anvendt på AI. Tillit, integritet, tilgjengelighet og håndkraft.",
          "subtopics": [
            "confidentiality-controls",
            "integrity-verification",
            "availability-requirements",
            "key-management"
          ]
        },
        {
          "id": "anskaffelser-ai-procurement-framework",
          "title": "AI Procurement Framework - Norwegian Public Sector Guidelines",
          "description": "Veileder for anskaffelse av AI-løsninger i offentlig sektor. Konkurranseregler, lisensmodeller, leverandørvurdering og kontraktskrav.",
          "subtopics": [
            "competitive-bidding-requirements",
            "vendor-evaluation-criteria",
            "licensing-models",
            "contract-management"
          ]
        },
        {
          "id": "gevinstrealisering-ai-projects",
          "title": "Benefits Realization - AI Value Capture in Public Organizations",
          "description": "Metoder for måling og realisering av gevinster fra AI-implementeringer. KPI-er, verdikjeder og organisatorisk endringsledelse.",
          "subtopics": [
            "benefit-definition-measurement",
            "kpi-frameworks",
            "value-chain-mapping",
            "change-management-strategy"
          ]
        },
        {
          "id": "norge-ai-strategy-government",
          "title": "Norwegian Government AI Strategy - Implementation Framework",
          "description": "Regjeringens strategi for AI i offentlig forvaltning. Prioriterte områder, finansiering, kompetanse og internasjonalt samarbeid.",
          "subtopics": [
            "strategic-priorities",
            "funding-mechanisms",
            "capability-building",
            "international-cooperation"
          ]
        },
        {
          "id": "digdir-ai-governance-structure",
          "title": "Digdir AI Governance - Organizational and Decision Structures",
          "description": "Digdirs modell for styring av AI i offentlig sektor. Roller, ansvar, eskalering og koordinering mellom departement og virksomheter.",
          "subtopics": [
            "governance-model",
            "decision-making-levels",
            "stakeholder-coordination",
            "accountability-framework"
          ]
        },
        {
          "id": "statistical-ethics-ssa-methodology",
          "title": "Statistics Norway (SSB) Ethics - Data and AI Methodology",
          "description": "Statistisk sentralbyrå sine etiske retningslinjer for statistikk og AI. Personvern, datakvalitet og etisk bruk av offentlige data.",
          "subtopics": [
            "data-quality-standards",
            "privacy-preservation-techniques",
            "statistical-disclosure-control",
            "ethical-guidelines"
          ]
        },
        {
          "id": "public-sector-ai-ethics-framework",
          "title": "Public Sector AI Ethics - Accountability and Transparency Standards",
          "description": "Norske etikkstandarder for offentlig sektor AI. Åpenhet, ansvar, ikke-diskriminering og borgernes tillitt.",
          "subtopics": [
            "transparency-requirements",
            "accountability-mechanisms",
            "bias-mitigation-audits",
            "citizen-trust-building"
          ]
        },
        {
          "id": "accessibility-requirements-wcag-norway",
          "title": "Accessibility Requirements - WCAG and Norwegian Legislation",
          "description": "Krav til universell utforming i AI-løsninger. WCAG 2.1, likestillingsloven og Digdir retningslinjer for offentlige IKT-løsninger.",
          "subtopics": [
            "wcag-2-1-compliance",
            "universal-design-standards",
            "assistive-technology-support",
            "testing-methodology"
          ]
        },
        {
          "id": "copyright-ai-training-data-norway",
          "title": "Copyright and AI Training Data - Norwegian Legal Framework",
          "description": "Rettigheter og restriksjoner ved bruk av tredjepartsdata og opphavsrettslig materiale i AI-trening. Norsk og EU-lovverk.",
          "subtopics": [
            "copyright-exceptions-ai",
            "fair-dealing-doctrine",
            "data-licensing-requirements",
            "third-party-consent"
          ]
        },
        {
          "id": "budget-and-accounting-ai-costs",
          "title": "Public Sector Budget and Accounting - AI Cost Allocation",
          "description": "Regnskapsmessig og budsjettprosessen for AI-prosjekter i offentlig sektor. Kapitaliseringsregler, avskrivninger og resultatmåling.",
          "subtopics": [
            "capitalization-criteria",
            "depreciation-schedules",
            "cost-allocation-methods",
            "financial-reporting"
          ]
        },
        {
          "id": "digital-accessibility-action-plan",
          "title": "Digital Accessibility Action Plan - Implementation Roadmap",
          "description": "Gjennomføring av tilgjengelighetsmål i AI-løsninger. Konkrete tiltak, ansvar, tidsplan og målgrupper.",
          "subtopics": [
            "accessibility-roadmap",
            "stakeholder-engagement",
            "testing-and-certification",
            "continuous-improvement"
          ]
        },
        {
          "id": "citizen-communication-ai-decisions",
          "title": "Citizen Communication Strategy - Explaining AI-Driven Decisions",
          "description": "Kommunikasjon til innbyggere om AI-bruk i offentlig forvaltning. Transparens, forklaring av vedtak og informasjon om rettigheter.",
          "subtopics": [
            "plain-language-explanations",
            "decision-rationale-communication",
            "citizen-rights-information",
            "feedback-mechanisms"
          ]
        }
      ]
    },
    "ai-security-engineering": {
      "name": "AI Security Engineering",
      "dir": "ai-security-engineering",
      "priority": "HIGH",
      "skills": [
        {
          "id": "prompt-injection-defense-patterns",
          "title": "Prompt Injection Defense Patterns and Mitigation",
          "description": "Praktiske forsvarsmønstre mot prompt injection-angrep, inkludert input-validering, sandboxing, og prompt-struktur-herdning.",
          "subtopics": [
            "input-sanitization-techniques",
            "prompt-layering-and-isolation",
            "delimiters-and-escaping",
            "token-analysis-detection",
            "semantic-validation"
          ]
        },
        {
          "id": "jailbreak-prevention-production",
          "title": "Jailbreak Prevention in Production AI Systems",
          "description": "Operative kontroller for å detektere og forhindre jailbreak-forsøk, inkludert system message-herdning og oppførselsvalidering.",
          "subtopics": [
            "system-message-integrity-checks",
            "behavior-constraint-enforcement",
            "constraint-testing-frameworks",
            "adaptive-defense-mechanisms",
            "rollback-procedures"
          ]
        },
        {
          "id": "content-safety-filter-calibration",
          "title": "Content Safety Filter Calibration and Tuning",
          "description": "Kalibrering av Azure Content Safety og tilsvarende tjenester for norsk kontekst, håndtering av false positives/negatives.",
          "subtopics": [
            "threshold-optimization-methodology",
            "multilingual-safety-rules",
            "domain-specific-filtering",
            "bias-in-safety-filters",
            "feedback-loop-refinement"
          ]
        },
        {
          "id": "pii-detection-norwegian-context",
          "title": "PII Detection and Masking in Norwegian Text",
          "description": "Identifikasjon og maskering av personidentifiserbar informasjon i norsk og skandinavisk kontekst, NAV-nummer, personnummer, adresser.",
          "subtopics": [
            "norwegian-pii-patterns",
            "regex-and-ml-detection-hybrid",
            "masking-strategies",
            "structured-data-handling",
            "compliance-documentation"
          ]
        },
        {
          "id": "ai-threat-modeling-stride",
          "title": "AI Threat Modeling Using STRIDE Framework",
          "description": "Strukturert trusselmodellering spesifikk for AI-systemer, tilpasning av STRIDE til LLM-arkitektur.",
          "subtopics": [
            "ai-specific-threat-categories",
            "stride-adaptation-ai",
            "threat-probability-assessment",
            "mitigation-mapping",
            "documentation-templates"
          ]
        },
        {
          "id": "ai-security-scoring-framework",
          "title": "AI Security Scoring and Risk Rating Framework",
          "description": "Metodikk for å score og rangere AI-sikkerhetsrisiko, kvantitativ og kvalitativ vurdering av forsvarsstatus.",
          "subtopics": [
            "scoring-dimensions-selection",
            "quantitative-scoring-methodology",
            "risk-matrix-plotting",
            "trend-tracking-over-time",
            "stakeholder-reporting-templates"
          ]
        },
        {
          "id": "ai-incident-response-procedures",
          "title": "AI Incident Response and Breach Handling Procedures",
          "description": "Planlegging og prosedyrer for håndtering av sikkerhetsbrudd i AI-systemer, eskalering, kommunikasjon og etteranalyse.",
          "subtopics": [
            "incident-detection-triggers",
            "response-playbooks-ai-specific",
            "containment-strategies",
            "forensics-and-logging",
            "post-incident-analysis"
          ]
        },
        {
          "id": "output-validation-grounding-verification",
          "title": "Output Validation, Grounding Verification, and Fact-Checking",
          "description": "Teknikker for validering av AI-output mot kilder, sjekk for hallusinasjoner, grounding-verifisering.",
          "subtopics": [
            "semantic-grounding-checks",
            "source-attribution-verification",
            "citation-validation",
            "hallucination-detection-metrics",
            "automated-fact-checking"
          ]
        },
        {
          "id": "zero-trust-ai-services",
          "title": "Zero Trust Architecture Applied to AI Services",
          "description": "Zero Trust-prinsipper implementert for AI-tjenester, mikrosegmentering, autentisering, minste-privileg for AI-modeller.",
          "subtopics": [
            "ai-service-network-isolation",
            "managed-identity-rbac",
            "endpoint-verification-ai",
            "continuous-access-evaluation",
            "audit-logging-ai"
          ]
        },
        {
          "id": "data-leakage-prevention-ai",
          "title": "Data Leakage Prevention in AI Contexts",
          "description": "Strategi for å forhindre utilsiktet datalekkasje gjennom AI-output, kontekst-lekkasje, modellekstraksjonsangrep.",
          "subtopics": [
            "prompt-context-isolation",
            "model-extraction-defense",
            "membership-inference-protection",
            "dlp-policy-enforcement",
            "cache-security-management"
          ]
        },
        {
          "id": "adversarial-input-robustness-testing",
          "title": "Adversarial Input Robustness Testing and Fuzzing",
          "description": "Teststrategier for å finne svakheter ved å sende adversarielle input, fuzzing-teknikker, attack-surface-analyse.",
          "subtopics": [
            "adversarial-test-case-generation",
            "fuzzing-frameworks-ai",
            "input-perturbation-techniques",
            "robustness-metrics",
            "continuous-security-testing"
          ]
        },
        {
          "id": "model-fingerprinting-watermarking",
          "title": "Model Fingerprinting and Watermarking for Attribution",
          "description": "Teknikker for å legge inn fingeravtrykk eller vannmerker i AI-modeller for detektor av uautorisert bruk.",
          "subtopics": [
            "model-watermark-embedding",
            "detection-of-copies",
            "ownership-verification",
            "steganography-in-models",
            "legal-implications"
          ]
        },
        {
          "id": "secure-model-deployment-hardening",
          "title": "Secure Model Deployment and Runtime Hardening",
          "description": "Sikring av modelldeployment mot aangrep, container-sikkerhet, runtime-overflow-forsvar, ressurskvoter.",
          "subtopics": [
            "container-image-scanning",
            "runtime-memory-protection",
            "resource-exhaustion-defense",
            "model-integrity-verification",
            "secrets-management-in-deployment"
          ]
        },
        {
          "id": "ai-red-team-operations-practical",
          "title": "Practical Red Team Operations for AI Systems",
          "description": "Operativ veiledning for å gjennomføre red team-tester på AI-systemer, metodikk, dokumentasjon, rapportering.",
          "subtopics": [
            "red-team-methodology-ai",
            "attack-simulation-planning",
            "safe-testing-boundaries",
            "finding-documentation",
            "remediation-tracking"
          ]
        },
        {
          "id": "supply-chain-security-ai-models",
          "title": "Supply Chain Security for AI Models and Dependencies",
          "description": "Sikkerhet i forsyningskjeden for AI-modeller, dependency-management, forurensning-deteksjon, vendor-risiko.",
          "subtopics": [
            "model-provenance-tracking",
            "dependency-vulnerability-scanning",
            "vendor-security-assessment",
            "model-poisoning-prevention",
            "sbom-for-ai"
          ]
        }
      ]
    },
    "monitoring-observability": {
      "name": "Monitoring & Observability",
      "dir": "monitoring-observability",
      "priority": "HIGH",
      "skills": [
        {
          "id": "azure-monitor-setup-ai-workloads",
          "title": "Azure Monitor Setup and Configuration for AI Workloads",
          "description": "Hvordan sette opp Azure Monitor for AI-systemer, inkludert metrics collection, logging, og diagnostic settings for Azure AI Services og Copilot deployments.",
          "subtopics": [
            "monitor-configuration",
            "diagnostic-settings",
            "metrics-collection",
            "log-ingestion",
            "resource-tagging"
          ]
        },
        {
          "id": "application-insights-llm-monitoring",
          "title": "Application Insights for LLM and Copilot Applications",
          "description": "Instrumentering av AI-applikasjoner med Application Insights, sporing av model calls, latency, og brukeradferd.",
          "subtopics": [
            "ai-instrumentation",
            "dependency-tracking",
            "custom-events",
            "user-telemetry",
            "performance-monitoring"
          ]
        },
        {
          "id": "token-usage-tracking-attribution",
          "title": "Token Usage Tracking and Cost Attribution",
          "description": "Overvåking av token-forbruk per model, user, project, og department med automatisk kostnadsattribusjon og budsjettvarslinger.",
          "subtopics": [
            "token-counting",
            "usage-tracking",
            "cost-allocation",
            "department-chargeback",
            "budget-alerts"
          ]
        },
        {
          "id": "anomaly-detection-ai-systems",
          "title": "Anomaly Detection and Alerting for AI Systems",
          "description": "Sette opp anomaly detection for modellperformanse, token-bruk, responstider, og compliance-brudd med intelligente varsler.",
          "subtopics": [
            "anomaly-detection",
            "threshold-alerting",
            "intelligent-alerts",
            "baseline-tuning",
            "incident-response"
          ]
        },
        {
          "id": "custom-dashboards-ai-operations",
          "title": "Custom Dashboards and Visualizations for AI Operations",
          "description": "Bygge operative dashboards i Azure Monitor og Power BI for AI-helse, brukeranalyser, kostnader og compliance-status.",
          "subtopics": [
            "dashboard-design",
            "kql-queries",
            "power-bi-integration",
            "real-time-visualization",
            "executive-reporting"
          ]
        },
        {
          "id": "log-analytics-kql-ai-queries",
          "title": "Log Analytics KQL Queries for AI Workloads",
          "description": "Skrive effektive KQL-spørringer for å analysere AI-aktivitet, feilsøk problemer, og trekke ut innsikter fra Azure Monitor Logs.",
          "subtopics": [
            "kql-syntax",
            "performance-queries",
            "error-analysis",
            "audit-queries",
            "query-optimization"
          ]
        },
        {
          "id": "distributed-tracing-ai-pipelines",
          "title": "Distributed Tracing Across AI Pipelines and Agents",
          "description": "Implementering av distribuert tracing for multi-step AI-workflows, agent-orkestrering, og RAG-pipelines med korrelerings-ID tracking.",
          "subtopics": [
            "correlation-ids",
            "trace-propagation",
            "end-to-end-tracing",
            "opentelemetry",
            "service-dependencies"
          ]
        },
        {
          "id": "sla-monitoring-ai-services",
          "title": "SLA Monitoring and Availability Tracking for AI Services",
          "description": "Overvåking av serviceavtaler for AI-tjenester, oppetidsmåling, og ytelsesgarantier med compliance-rapportering.",
          "subtopics": [
            "availability-metrics",
            "uptime-tracking",
            "sla-compliance",
            "latency-slo",
            "incident-tracking"
          ]
        },
        {
          "id": "model-performance-drift-detection",
          "title": "Model Performance Monitoring and Drift Detection",
          "description": "Deteksjon av modell-drift, degradasjon av output-kvalitet, og endringer i brukeradferd over tid.",
          "subtopics": [
            "drift-metrics",
            "quality-baselines",
            "output-validation",
            "performance-degradation",
            "retraining-triggers"
          ]
        },
        {
          "id": "security-and-audit-logging-ai",
          "title": "Security and Audit Logging for AI Systems",
          "description": "Revisjonssporing av tilgang, API-bruk, datauttak, og compliance med GDPR/AI Act gjennom strukturert logging.",
          "subtopics": [
            "access-logging",
            "audit-trails",
            "data-lineage",
            "compliance-logging",
            "threat-detection"
          ]
        },
        {
          "id": "cost-monitoring-cost-attribution",
          "title": "Cost Monitoring and Expense Reporting for AI Deployments",
          "description": "Detaljert kostnadsovervåking per model, endpoint, project, og bruker med automatisk rapportering og kostnadsoptimalisering.",
          "subtopics": [
            "cost-tracking",
            "expense-reporting",
            "consumption-analysis",
            "price-tracking",
            "optimization-recommendations"
          ]
        },
        {
          "id": "response-quality-metrics-rag",
          "title": "Response Quality Metrics and Evaluation for RAG Systems",
          "description": "Måling av RAG-svar-kvalitet, relevans, hallucination-rate, og bruker-feedback integration for kontinuerlig forbedring.",
          "subtopics": [
            "quality-scoring",
            "hallucination-detection",
            "relevance-metrics",
            "user-feedback",
            "quality-thresholds"
          ]
        },
        {
          "id": "endpoint-health-and-capacity-planning",
          "title": "Endpoint Health Monitoring and Capacity Planning",
          "description": "Overvåking av Azure OpenAI endpoints, deployment health, quotas, og ressurs-kapasitet med kapasitetsplanlegging.",
          "subtopics": [
            "endpoint-metrics",
            "quota-tracking",
            "throttling-alerts",
            "capacity-forecasting",
            "scaling-decisions"
          ]
        },
        {
          "id": "real-time-streaming-monitoring",
          "title": "Real-Time Streaming and Live Monitoring Dashboards",
          "description": "Implementering av live dashboards for sanntidsovervåking av AI-aktivitet, brukerinteraksjoner, og system-helse.",
          "subtopics": [
            "live-dashboards",
            "real-time-data",
            "streaming-queries",
            "websocket-updates",
            "alert-orchestration"
          ]
        },
        {
          "id": "compliance-monitoring-ai-governance",
          "title": "Compliance Monitoring and AI Governance Dashboards",
          "description": "Kontinuerlig overvåking av AI Act-compliance, dataminimerering, og governance-policy-etterlevelse.",
          "subtopics": [
            "policy-monitoring",
            "data-governance",
            "access-controls",
            "compliance-reports",
            "regulatory-tracking"
          ]
        },
        {
          "id": "alerting-strategies-escalation",
          "title": "Alerting Strategies and Escalation Policies for AI Incidents",
          "description": "Design av varslingsstrategi med eskalering, on-call rotasjoner, og incident-management-integrasjon for AI-systemer.",
          "subtopics": [
            "alert-routing",
            "escalation-policies",
            "on-call-management",
            "incident-integration",
            "notification-channels"
          ]
        },
        {
          "id": "observability-for-copilot-extensions",
          "title": "Observability Patterns for Copilot Extensions and Plugins",
          "description": "Spesialisert observabilitet for Copilot Studio extensions, plugins, og custom connectors med end-to-end tracing.",
          "subtopics": [
            "extension-tracing",
            "plugin-monitoring",
            "connector-health",
            "user-adoption-metrics",
            "extension-performance"
          ]
        },
        {
          "id": "data-residency-audit-monitoring",
          "title": "Data Residency and Geographic Audit Monitoring",
          "description": "Overvåking av dataresidency-compliance, geografisk dataplassering, og compliance med norske/EØS-krav.",
          "subtopics": [
            "data-location-tracking",
            "residency-compliance",
            "cross-region-monitoring",
            "audit-logs",
            "data-sovereignty"
          ]
        }
      ]
    },
    "agent-orchestration": {
      "name": "Agent Orchestration & Automation",
      "dir": "agent-orchestration",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "multi-agent-orchestration-patterns",
          "title": "Multi-Agent Orchestration Patterns and Topologies",
          "description": "Designmønstre for orkestrering av flere agenter, kommunikasjonstopologier, koordinering og asynkron samhandling.",
          "subtopics": [
            "hierarchical-orchestration",
            "peer-to-peer-coordination",
            "publish-subscribe-patterns",
            "workflow-orchestration",
            "state-management"
          ]
        },
        {
          "id": "agent-to-agent-communication",
          "title": "Agent-to-Agent Communication Protocols",
          "description": "Kommunikasjonsmekanismer mellom agenter, meldingsformat, API-kontrakter og interoperabilitet.",
          "subtopics": [
            "message-passing-protocols",
            "rest-vs-event-driven",
            "schema-validation",
            "timeout-retry-logic",
            "circuit-breaker-patterns"
          ]
        },
        {
          "id": "semantic-kernel-agents-implementation",
          "title": "Semantic Kernel and Microsoft Agent Framework - Implementation Patterns",
          "description": "Praktisk implementering av agenter med Semantic Kernel og Microsoft Agent Framework, plugin-arkitektur og function calling.",
          "subtopics": [
            "semantic-kernel-core-concepts",
            "agent-framework-lifecycle",
            "plugin-development-patterns",
            "function-calling-orchestration",
            "kernel-memory-integration"
          ]
        },
        {
          "id": "agent-memory-and-context-management",
          "title": "Agent Memory and Context Management Strategies",
          "description": "Hukommelsesarkitekturer for agenter, kontekstvinduoptimalisering, persistent state og episodisk minneing.",
          "subtopics": [
            "short-term-memory-sliding-windows",
            "long-term-memory-vector-stores",
            "episodic-memory-persistence",
            "context-compression",
            "memory-retrieval-strategies"
          ]
        },
        {
          "id": "tool-use-and-function-calling-patterns",
          "title": "Tool Use and Function Calling - Advanced Patterns",
          "description": "Avanserte mønstre for verktøybruk, function calling, tool-chaining og error handling i agent-kontekst.",
          "subtopics": [
            "parallel-tool-execution",
            "tool-chaining-sequences",
            "tool-result-validation",
            "fallback-mechanisms",
            "tool-capability-negotiation"
          ]
        },
        {
          "id": "agent-autonomy-and-control-governance",
          "title": "Agent Autonomy and Control - Governance Framework",
          "description": "Styring av agentautonomi, sikkerhet, grenser for agenthandlinger og human-in-the-loop-integrasjon.",
          "subtopics": [
            "action-approval-workflows",
            "scope-and-capability-limits",
            "audit-trail-logging",
            "rollback-mechanisms",
            "human-override-patterns"
          ]
        },
        {
          "id": "agent-365-governance-and-deployment",
          "title": "Agent 365 Governance and Enterprise Deployment",
          "description": "Agent 365-arkitektur, governance, sikkerhet, skalering og integrasjon med M365-rettigheter.",
          "subtopics": [
            "agent-365-architecture",
            "enterprise-permissions-model",
            "lifecycle-management",
            "versioning-and-rollout",
            "compliance-and-audit"
          ]
        },
        {
          "id": "agent-evaluation-testing-frameworks",
          "title": "Agent Evaluation and Testing Frameworks",
          "description": "Evaluering av agentytelse, testramme verk, suksesskriterier og A/B-testing for multi-agent-systemer.",
          "subtopics": [
            "agent-performance-metrics",
            "end-to-end-testing",
            "regression-testing",
            "user-satisfaction-measurement",
            "cost-efficiency-evaluation"
          ]
        },
        {
          "id": "autonomous-workflow-automation-patterns",
          "title": "Autonomous Workflow Automation Patterns",
          "description": "Designmønstre for fullautomatiske arbeidsflytkjeder, triggerbaserte automatisering og event-driven arkitektur.",
          "subtopics": [
            "event-trigger-mechanisms",
            "conditional-routing",
            "parallel-branch-execution",
            "error-recovery-flows",
            "escalation-rules"
          ]
        },
        {
          "id": "agent-feedback-and-learning-loops",
          "title": "Agent Feedback and Continuous Learning Loops",
          "description": "Mekanismer for tilbakemelding, læring fra agenthandlinger, RLHF-integrasjon og kontinuerlig forbedring.",
          "subtopics": [
            "human-feedback-collection",
            "reward-modeling",
            "performance-monitoring",
            "drift-detection",
            "retraining-triggers"
          ]
        },
        {
          "id": "multi-tenant-agent-isolation",
          "title": "Multi-Tenant Agent Isolation and Security",
          "description": "Sikker isolasjon av agenter i multi-tenant-miljøer, dataprivacy, RBAC og sikkerhetsgrenserfortelling.",
          "subtopics": [
            "tenant-data-isolation",
            "permission-enforcement",
            "audit-segregation",
            "cross-tenant-attack-prevention",
            "resource-quotas"
          ]
        },
        {
          "id": "agent-routing-and-specialization",
          "title": "Agent Routing and Task Specialization",
          "description": "Intelligente rutingstrategier mellom spesialiserte agenter, oppgaveklassifisering og skill-matching.",
          "subtopics": [
            "intent-classification-routing",
            "agent-capability-matching",
            "load-balancing-strategies",
            "fallback-routing",
            "specialization-hierarchies"
          ]
        },
        {
          "id": "agent-latency-optimization",
          "title": "Agent Latency Optimization and Performance Tuning",
          "description": "Optimalisering av responstid for agenter, parallellisering, caching og asynchronous operasjoner.",
          "subtopics": [
            "request-batching",
            "response-streaming",
            "prefetching-strategies",
            "cache-invalidation",
            "async-awaitable-patterns"
          ]
        },
        {
          "id": "agent-monitoring-observability",
          "title": "Agent Monitoring, Observability and Debugging",
          "description": "Observabilitet for agent-systemer, logg inggrep, tracing, feilsøking og performance monitoring.",
          "subtopics": [
            "distributed-tracing-agents",
            "agent-event-logging",
            "performance-profiling",
            "error-categorization",
            "debugging-tools"
          ]
        },
        {
          "id": "copilot-agent-integration-patterns",
          "title": "Copilot Agent Integration Patterns",
          "description": "Integrasjon av agenter med Copilot Studio, M365 Copilot og Copilot-baserte løsninger.",
          "subtopics": [
            "copilot-studio-agent-binding",
            "message-format-adaptation",
            "capability-exposure",
            "user-context-passing",
            "session-management"
          ]
        },
        {
          "id": "agent-cost-optimization-strategies",
          "title": "Agent Cost Optimization and Resource Management",
          "description": "Kostnadsoptimalisering for agent-systemer, modellvalg, token-effektivitet og ressursallokering.",
          "subtopics": [
            "model-selection-per-task",
            "token-optimization-agents",
            "request-deduplication",
            "resource-pooling",
            "cost-attribution-per-agent"
          ]
        },
        {
          "id": "declarative-vs-imperative-agent-design",
          "title": "Declarative vs Imperative Agent Design Tradeoffs",
          "description": "Sammenligning av deklarativ agent-design (Copilot Studio) versus imperativ (code-first) med trade-offs og use cases.",
          "subtopics": [
            "declarative-agent-benefits",
            "code-first-flexibility",
            "migration-paths",
            "hybrid-approaches",
            "skill-abstraction-levels"
          ]
        },
        {
          "id": "agent-security-threat-modeling",
          "title": "Agent Security and Threat Modeling",
          "description": "Sikkerhetstrusler spesifikk for agent-systemer, threat modeling, injection-angrep og mitigation-strategier.",
          "subtopics": [
            "agent-prompt-injection",
            "tool-abuse-prevention",
            "credential-handling",
            "data-exfiltration-risks",
            "agent-impersonation-attacks"
          ]
        },
        {
          "id": "agent-compliance-and-audit-trails",
          "title": "Agent Compliance and Audit Trail Management",
          "description": "Compliance-krav for agentstyrte operasjoner, revisjonslogg, dokumentasjon og etterlevelsesrammeverk.",
          "subtopics": [
            "action-audit-logging",
            "decision-trail-documentation",
            "retention-policies",
            "regulatory-alignment",
            "compliance-reporting"
          ]
        },
        {
          "id": "agent-ecosystem-and-marketplace",
          "title": "Agent Ecosystem and Plugin Marketplace Patterns",
          "description": "Bygging av agentekosystemer, plugin-markeder, third-party-integrasjoner og distribusjon av agentplugins.",
          "subtopics": [
            "plugin-discovery-mechanisms",
            "capability-advertisement",
            "dependency-management",
            "version-compatibility",
            "revenue-sharing-models"
          ]
        }
      ]
    },
    "bcdr": {
      "name": "Business Continuity & Disaster Recovery",
      "dir": "bcdr",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "multi-region-azure-openai-deployment",
          "title": "Multi-Region Azure OpenAI Deployment",
          "description": "Strategi for distribusjon av Azure OpenAI-ressurser over multiple regioner for høy tilgjengelighet.",
          "subtopics": [
            "Azure region selection for Norway and EU",
            "Load balancing across OpenAI endpoints",
            "Latency optimization and proximity routing",
            "Quota management per region",
            "Cost modeling for multi-region setup"
          ]
        },
        {
          "id": "ai-foundry-disaster-recovery-planning",
          "title": "AI Foundry Disaster Recovery Planning",
          "description": "Comprehensive DR-strategi for Azure AI Foundry prosjekter med fokus på prosjektdata, modeller og konfigurasjoner.",
          "subtopics": [
            "Project data backup and replication",
            "Model version control and recovery",
            "Configuration as code for reproducibility",
            "RTO and RPO definitions for AI projects",
            "Testing and validation of DR procedures"
          ]
        },
        {
          "id": "backup-recovery-strategies-ai-workloads",
          "title": "Backup and Recovery Strategies for AI Workloads",
          "description": "Praktiske backup-strategier for AI-data, modeller og deployment-konfigurasjoner.",
          "subtopics": [
            "Incremental vs full backup approaches",
            "Point-in-time recovery for datasets",
            "Snapshot management and retention",
            "Off-region backup storage",
            "Automation and scheduling of backups"
          ]
        },
        {
          "id": "failover-testing-ai-services",
          "title": "Failover Testing for AI Services",
          "description": "Metodikk for planlagte failover-tester av Azure OpenAI og AI Foundry-tjenester.",
          "subtopics": [
            "Planned failover test scenarios",
            "Validation and monitoring during failover",
            "Success criteria and acceptance thresholds",
            "Documentation and lessons learned",
            "Regular test scheduling and cadence"
          ]
        },
        {
          "id": "rto-rpo-planning-ai-services",
          "title": "RTO and RPO Planning for AI Services",
          "description": "Definering av Recovery Time Objective og Recovery Point Objective for AI-systemer basert på forretningskritikalitet.",
          "subtopics": [
            "Business impact analysis for RTO determination",
            "Data loss tolerance and RPO calculation",
            "Mapping requirements to Azure capabilities",
            "Norwegian regulatory compliance",
            "Documentation templates and governance"
          ]
        },
        {
          "id": "data-replication-patterns-ai",
          "title": "Data Replication Patterns for AI Systems",
          "description": "Datareplikasjons-mønstre for AI-arbeidsbelastninger inkludert synkron, asynkron og hybrid-tilnærminger.",
          "subtopics": [
            "Synchronous vs asynchronous replication",
            "Active-active and active-passive patterns",
            "Consistency models and eventual consistency",
            "Conflict resolution strategies",
            "Monitoring replication lag and health"
          ]
        },
        {
          "id": "geo-redundancy-azure-ai-search",
          "title": "Geo-Redundancy for Azure AI Search",
          "description": "Implementering av geografisk redundans for Azure AI Search-indekser med failover og load-balancing.",
          "subtopics": [
            "Index replication across regions",
            "Replica count sizing for availability",
            "Failover and routing strategies",
            "Keeping indices synchronized",
            "Query performance in multi-region setup"
          ]
        },
        {
          "id": "incident-response-ai-systems",
          "title": "Incident Response for AI Systems",
          "description": "Incident response-prosedyrer spesifikt for AI-systemer og LLM-tjenester.",
          "subtopics": [
            "AI-specific incident classifications",
            "Detection and alerting strategies",
            "Escalation procedures and runbooks",
            "Communication plans for stakeholders",
            "Post-incident review and improvement"
          ]
        },
        {
          "id": "capacity-planning-dr-configurations",
          "title": "Capacity Planning for DR Configurations",
          "description": "Kapasitetsplanlegging for DR-miljøer med fokus på dimensjonering av reserve-ressurser.",
          "subtopics": [
            "Sizing DR environment for peak load",
            "Surge capacity and burst handling",
            "Cost optimization for standby resources",
            "Scaling policies and auto-scaling rules",
            "Capacity reservation strategies"
          ]
        },
        {
          "id": "compliance-requirements-bcdr",
          "title": "Compliance Requirements for BCDR in Norwegian Public Sector",
          "description": "Sammenfattende oversikt over norske BCDR-krav for offentlige organisasjoner.",
          "subtopics": [
            "Forvaltningsloven requirements for continuity",
            "GDPR and data residency requirements",
            "NSM security guidelines for critical infrastructure",
            "Sector-specific regulations",
            "Audit and documentation requirements"
          ]
        },
        {
          "id": "network-resilience-patterns-ai",
          "title": "Network Resilience Patterns for AI Workloads",
          "description": "Nettverksmønstre for resilient AI-løsninger inkludert redundante forbindelser og graceful degradation.",
          "subtopics": [
            "Redundant network paths and connectivity",
            "Circuit breaker patterns for API calls",
            "Graceful degradation of AI services",
            "Private endpoints and network isolation",
            "DDoS protection and traffic filtering"
          ]
        },
        {
          "id": "state-management-failover",
          "title": "State Management and Consistency During Failover",
          "description": "Håndtering av application state under failover-scenarioer for AI-systemer.",
          "subtopics": [
            "Distributed state management patterns",
            "Session state replication and synchronization",
            "Handling in-flight requests during failover",
            "Idempotency and request retry strategies",
            "State validation and verification procedures"
          ]
        },
        {
          "id": "monitoring-alerting-failover-detection",
          "title": "Monitoring and Alerting for Failover Detection",
          "description": "Monitoringstrategier for rask oppdagelse av feil og automatisk failover-initiering.",
          "subtopics": [
            "Health check endpoints and heartbeats",
            "Latency and error rate monitoring",
            "Custom metrics for AI service health",
            "Alert rules and escalation policies",
            "Integration with incident management systems"
          ]
        },
        {
          "id": "cost-analysis-dr-configurations",
          "title": "Cost Analysis and Optimization for DR Configurations",
          "description": "Kostnadsanalyse av BCDR-løsninger for AI-systemer.",
          "subtopics": [
            "Total cost of ownership calculation",
            "RTO/RPO vs cost trade-off analysis",
            "Reserved capacity vs on-demand pricing",
            "Cross-region bandwidth costs",
            "Cost optimization and reserved instances"
          ]
        },
        {
          "id": "chaos-engineering-ai-systems",
          "title": "Chaos Engineering for AI Systems",
          "description": "Strukturert chaos engineering og resilience testing for AI-løsninger.",
          "subtopics": [
            "Fault injection strategies for AI services",
            "Network partition simulation",
            "Load and stress testing methodologies",
            "Recovery time measurement and validation",
            "Tools and platforms for chaos engineering"
          ]
        },
        {
          "id": "service-level-documentation-dr",
          "title": "Service Level Documentation and DR Runbooks",
          "description": "Dokumentering av SLA, RTO, RPO og operasjonelle runbooks for AI-systemer.",
          "subtopics": [
            "Service Level Agreement templates",
            "RTO and RPO documentation standards",
            "Disaster recovery runbooks and playbooks",
            "Step-by-step recovery procedures",
            "Ownership and escalation matrix"
          ]
        }
      ]
    },
    "data-engineering": {
      "name": "Data Engineering for AI",
      "dir": "data-engineering",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "fabric-lakehouse-architecture",
          "title": "Fabric Lakehouse Architecture for AI Workloads",
          "description": "Designmønstre for OneLake-baserte dataproduksjonsarkitekturer på Microsoft Fabric.",
          "subtopics": [
            "OneLake design principles and data organization",
            "Medallion layering and lakehouse per-layer strategies",
            "Workspace topology for governance and separation",
            "Direct Lake query optimization for AI models",
            "Shortcuts and data sharing patterns"
          ]
        },
        {
          "id": "onelake-data-strategy",
          "title": "OneLake Data Strategy and Shortcuts",
          "description": "Implementering av OneLake som sentralt datarepositorium for AI-løsninger.",
          "subtopics": [
            "OneLake shortcut creation and management",
            "External data sharing across tenants",
            "OneLake RBAC and permission models",
            "Cross-workspace data consumption patterns",
            "Metadata shortcuts versus data copies"
          ]
        },
        {
          "id": "data-factory-ai-pipelines",
          "title": "Data Factory AI-Driven Pipelines",
          "description": "Automatisering av dataintegrerings- og transformasjonspipelines i Microsoft Data Factory.",
          "subtopics": [
            "Copy Activity with incremental load and CDC",
            "Data Factory connectors",
            "Mapping Data Flows for transformation",
            "Parameterization and dynamic pipelines",
            "AI-assisted pipeline generation and monitoring"
          ]
        },
        {
          "id": "zero-etl-fabric-patterns",
          "title": "Zero-ETL Patterns with Microsoft Fabric",
          "description": "Implementering av Zero-ETL-strategier med Fabric Mirroring og native integrasjon.",
          "subtopics": [
            "Database Mirroring for transactional systems",
            "Continuous replication into OneLake",
            "Real-time Bronze layer ingestion",
            "CDC targets to Lakehouse",
            "Mirroring vs Copy Activity tradeoffs"
          ]
        },
        {
          "id": "data-quality-ai-frameworks",
          "title": "Data Quality Frameworks for AI",
          "description": "Etablering av datakvalitetsstandarder og valideringsprosesser tilpasset AI-modelltrening.",
          "subtopics": [
            "Schema validation and enforcement",
            "Nullability and completeness checks",
            "Outlier detection and anomaly flagging",
            "Data lineage and impact analysis",
            "Quality metrics and SLIs for ML pipelines"
          ]
        },
        {
          "id": "real-time-streaming-ai",
          "title": "Real-Time Streaming for AI Applications",
          "description": "Integrering av Event Hubs, Kafka, og Fabric Eventstream for realtids datainnstrømming til AI-modeller.",
          "subtopics": [
            "Eventstream connectors and topologies",
            "Structured Streaming with Spark",
            "KQL Database for time-series analytics",
            "Event filtering and derived streams",
            "Streaming SLAs and backpressure handling"
          ]
        },
        {
          "id": "dataverse-ai-integration",
          "title": "Dataverse and AI Integration",
          "description": "Kobling av Microsoft Dataverse-data til AI-løsninger via Data Factory og Fabric.",
          "subtopics": [
            "Dataverse connectors in Data Factory",
            "Entity relationship mapping to Delta tables",
            "Real-time Dataverse data sync",
            "Power Platform data integration",
            "RLS propagation from Dataverse to Fabric"
          ]
        },
        {
          "id": "lakehouse-architecture-design",
          "title": "Lakehouse Architecture Design and Patterns",
          "description": "Arkitekturdesign som kombinerer datalake- og datawarehouse-egenskaper med ACID-garantier.",
          "subtopics": [
            "Delta Lake transaction semantics",
            "Schema-on-read versus schema-on-write tradeoffs",
            "Time-travel and data versioning",
            "Upsert and merge patterns for slowly-changing dimensions",
            "Lakehouse performance tuning"
          ]
        },
        {
          "id": "microsoft-purview-governance",
          "title": "Microsoft Purview Data Governance",
          "description": "Implementering av datahersking og klassifisering med Microsoft Purview.",
          "subtopics": [
            "Purview catalog and asset registration",
            "Data classification and sensitivity labels",
            "Lineage tracking across Fabric",
            "Policy enforcement and access management",
            "GDPR/HIPAA compliance auditing"
          ]
        },
        {
          "id": "synthetic-data-generation",
          "title": "Synthetic Data Generation for AI Training",
          "description": "Teknikker for generering av syntetiske datasett for å utvide treningsdata.",
          "subtopics": [
            "Synthetic data generation pipelines",
            "Azure OpenAI integration for text synthesis",
            "Balancing class imbalances with synthetic samples",
            "Privacy-preserving synthetic data",
            "Validation of synthetic data quality"
          ]
        },
        {
          "id": "feature-stores-engineering",
          "title": "Feature Stores and Feature Engineering",
          "description": "Design og implementering av feature store-mønstre på Fabric.",
          "subtopics": [
            "Feature definition and storage in Silver layer",
            "Point-in-time lookups for training",
            "Feature freshness and refresh cadences",
            "Data Wrangler for exploratory feature engineering",
            "Feature monitoring and drift detection"
          ]
        },
        {
          "id": "data-versioning-lineage",
          "title": "Data Versioning and Lineage Tracking",
          "description": "Implementering av dataversionskontroll og komplett lineage-tracking.",
          "subtopics": [
            "Delta Lake versioning and time-travel",
            "Commit history and audit trails",
            "Data lineage visualization in Purview",
            "Upstream/downstream dependency mapping",
            "Rollback and recovery strategies"
          ]
        },
        {
          "id": "etl-vs-elt-ai",
          "title": "ETL vs ELT Strategies for AI Workloads",
          "description": "Evaluering av tradisjonell ETL mot moderne ELT på Fabric.",
          "subtopics": [
            "ELT advantages: cost, scalability, schema-flexibility",
            "ETL data minimization for regulated environments",
            "Hybrid ETL/ELT patterns",
            "Data staging and incremental processing",
            "Compute cost allocation: ETL vs ELT"
          ]
        },
        {
          "id": "data-cataloging-discovery",
          "title": "Data Cataloging and Discovery",
          "description": "Sentrale datakatalogiserings- og oppdagelsesmekanismer ved bruk av Purview og Fabric metadata.",
          "subtopics": [
            "Asset registration and metadata enrichment",
            "Search and discovery interfaces",
            "Business glossaries and taxonomies",
            "Data owner and steward assignments",
            "Usage analytics and popularity metrics"
          ]
        },
        {
          "id": "delta-lake-parquet-optimization",
          "title": "Delta Lake and Parquet Format Optimization",
          "description": "Optimering av Delta Lake og Parquet-filformater for ytelse, lagring og kostnader.",
          "subtopics": [
            "Delta Lake ACID transactions and Z-order",
            "Parquet compression codecs and row groups",
            "File size tuning and auto-compaction",
            "V-Order optimization for sort order",
            "Small file handling and garbage collection"
          ]
        },
        {
          "id": "data-mesh-patterns",
          "title": "Data Mesh Patterns and Domain Ownership",
          "description": "Implementering av data mesh-arkitektur med autonome domeener som eier sine dataprodukter.",
          "subtopics": [
            "Domain-oriented data ownership",
            "Data product versioning and contracts",
            "Cross-domain data sharing via shortcuts",
            "Federated governance and shared platform",
            "Scaling to 50+ domains with OneLake"
          ]
        },
        {
          "id": "master-data-management-ai",
          "title": "Master Data Management for AI",
          "description": "Sentrale MDM-strategier for å opprettholde enkeltkilder for kritiske enheter.",
          "subtopics": [
            "Golden record creation and reconciliation",
            "Entity resolution and deduplication",
            "MDM integration with Dataverse",
            "Reference data versioning",
            "Data quality SLAs for MDM entities"
          ]
        },
        {
          "id": "data-pipeline-orchestration",
          "title": "Data Pipeline Orchestration and Scheduling",
          "description": "Orkestrering av komplekse datapipelines med avhengighetsstyring og feiltoleranser.",
          "subtopics": [
            "Pipeline scheduling and triggers",
            "Dependency chains and critical paths",
            "Retry policies and error handling",
            "Monitoring and alerting on pipeline health",
            "SLAs and timeliness guarantees"
          ]
        },
        {
          "id": "data-sampling-labeling",
          "title": "Data Sampling and Labeling Strategies",
          "description": "Teknikker for effektiv datautvalg og merkingsprosesser for ML-treningsdatasett.",
          "subtopics": [
            "Stratified sampling for class balance",
            "Active learning and uncertainty sampling",
            "Crowdsourcing and labeling platforms",
            "Quality control and inter-rater agreement",
            "Feedback loops for continuous labeling"
          ]
        },
        {
          "id": "schema-evolution-management",
          "title": "Schema Evolution and Management",
          "description": "Håndtering av skjemaendringer over tid i Delta Lake-tabeller.",
          "subtopics": [
            "Schema versioning and compatibility levels",
            "Adding columns with default values",
            "Type promotions and narrowing",
            "Deprecated column handling",
            "Schema registration and validation"
          ]
        },
        {
          "id": "data-anonymization-privacy",
          "title": "Data Anonymization and Privacy Compliance",
          "description": "Teknikker for anonymisering og personvernbeskyttelse under GDPR.",
          "subtopics": [
            "Differential privacy techniques",
            "K-anonymity and l-diversity",
            "PII detection and masking",
            "Right-to-be-forgotten implementation",
            "Privacy impact assessments"
          ]
        },
        {
          "id": "cross-cloud-data-integration",
          "title": "Cross-Cloud Data Integration",
          "description": "Integrering av data fra AWS, Google Cloud og andre skyplattformer inn i Fabric OneLake.",
          "subtopics": [
            "Multi-cloud connector strategies",
            "Data egress cost optimization",
            "Consistency and synchronization patterns",
            "Hybrid cloud fallback mechanisms",
            "Data residency and sovereignty compliance"
          ]
        }
      ]
    },
    "api-management": {
      "name": "API Management & AI Gateway",
      "dir": "api-management",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "apim-ai-gateway-overview",
          "title": "APIM as AI Gateway: Architecture & Concepts",
          "description": "Grunnleggende arkitektur for API Management som AI-gateway.",
          "subtopics": [
            "APIM core concepts",
            "AI gateway patterns",
            "Multi-model backend routing",
            "Organizational governance",
            "Cost isolation"
          ]
        },
        {
          "id": "token-rate-limiting-policies",
          "title": "Token-Based Rate Limiting & Quota Policies",
          "description": "Implementering av token-basert rate limiting i APIM for AI-modeller.",
          "subtopics": [
            "Token counting in APIM",
            "Rate-limit-by-key policy",
            "Quota management",
            "Sliding window algorithms",
            "Burst allowances"
          ]
        },
        {
          "id": "load-balancing-openai-instances",
          "title": "Load Balancing Across Azure OpenAI Instances",
          "description": "Strategier for å distribuere forespørsler mellom multiple Azure OpenAI-instanser i APIM.",
          "subtopics": [
            "Backend pool configuration",
            "Round-robin vs weighted",
            "Health probes",
            "Deployment slot selection",
            "Regional distribution"
          ]
        },
        {
          "id": "circuit-breaker-ai-resilience",
          "title": "Circuit Breaker Patterns for AI Models",
          "description": "Implementering av circuit breaker-mønsteret i APIM for overbelastede AI-backends.",
          "subtopics": [
            "Circuit breaker state machine",
            "Failure threshold tuning",
            "Fallback policies",
            "Recovery mechanisms",
            "Timeout configuration"
          ]
        },
        {
          "id": "multi-region-ai-gateway-design",
          "title": "Multi-Region AI Gateway Architecture",
          "description": "Design av geografisk distribuert AI-gateway med APIM.",
          "subtopics": [
            "Global APIM distribution",
            "Region-aware routing",
            "Latency optimization",
            "Data residency compliance",
            "Cross-region failover"
          ]
        },
        {
          "id": "apim-authentication-oauth-managed-identity",
          "title": "APIM Authentication: OAuth, Azure AD & Managed Identity",
          "description": "Autentiseringsmønstre i APIM for AI-konsumenter.",
          "subtopics": [
            "Azure AD integration",
            "OAuth 2.0 flows",
            "Managed identity",
            "Client certificate auth",
            "API key rotation"
          ]
        },
        {
          "id": "backend-pool-management",
          "title": "Backend Pool Management & Health Probes",
          "description": "Konfigurering og overvåking av backend-pools i APIM for AI-tjenester.",
          "subtopics": [
            "Backend configuration",
            "Health probe policies",
            "Custom health checks",
            "Timeout and retry logic",
            "Pool metrics"
          ]
        },
        {
          "id": "streaming-support-apim",
          "title": "Streaming Support in APIM for AI Responses",
          "description": "Håndtering av Server-Sent Events og streaming-responser fra Azure OpenAI i APIM.",
          "subtopics": [
            "SSE forwarding",
            "Chunked responses",
            "Buffering policies",
            "Timeout management for streams",
            "Client compatibility"
          ]
        },
        {
          "id": "cost-tracking-apim-policies",
          "title": "Cost Tracking & Chargeback via APIM Policies",
          "description": "Innsamling av kostnadsdata fra AI-modeller via APIM-policyer.",
          "subtopics": [
            "Token counting from responses",
            "Model routing tracking",
            "Chargeback tagging",
            "Azure Cost Management integration",
            "Custom metrics"
          ]
        },
        {
          "id": "apim-vs-direct-access-comparison",
          "title": "APIM vs Direct Access: Trade-offs & Decision Matrix",
          "description": "Sammenlikning av API Management-modell mot direkte tilgang til Azure OpenAI.",
          "subtopics": [
            "Gateway overhead analysis",
            "Security posture comparison",
            "Governance requirements",
            "Cost per request",
            "Organizational scale factors"
          ]
        },
        {
          "id": "genai-gateway-policies",
          "title": "GenAI-Specific APIM Policies & Rules",
          "description": "APIM-policyer spesifikke for generativ AI inkludert content-filter og prompt-validering.",
          "subtopics": [
            "Content Safety integration",
            "Prompt validation policies",
            "Response filtering",
            "Rate limiting per model",
            "Audit logging for prompts"
          ]
        },
        {
          "id": "request-response-transformation-ai",
          "title": "Request/Response Transformation for AI APIs",
          "description": "Transformasjon av forespørsler og svar i APIM for standardiserte AI-API-grensesnitt.",
          "subtopics": [
            "Model-agnostic API schemas",
            "Header rewriting",
            "Payload transformation",
            "Error response normalization",
            "Version translation"
          ]
        },
        {
          "id": "caching-strategies-apim-ai",
          "title": "Caching Strategies for AI Responses in APIM",
          "description": "Implementering av caching-strategier for AI-svar i APIM.",
          "subtopics": [
            "Prompt-based caching keys",
            "Semantic deduplication",
            "TTL configuration",
            "Cache invalidation",
            "Cost savings analysis"
          ]
        },
        {
          "id": "logging-analytics-ai-traffic",
          "title": "Logging & Analytics for AI Traffic in APIM",
          "description": "Oppsett av logging og analysedashboards i APIM for AI-modellbruk.",
          "subtopics": [
            "Application Insights integration",
            "Custom metrics",
            "Token tracking",
            "Latency monitoring",
            "User behavior analysis"
          ]
        },
        {
          "id": "apim-azure-front-door-ai",
          "title": "APIM with Azure Front Door for Global AI Distribution",
          "description": "Kombinering av Azure Front Door og APIM for global AI-gateway-distribusjon.",
          "subtopics": [
            "Global load distribution",
            "DDoS protection",
            "Web Application Firewall",
            "Edge caching",
            "Geographic routing"
          ]
        },
        {
          "id": "developer-portal-ai-apis",
          "title": "Developer Portal for AI API Discovery & Onboarding",
          "description": "Konfigurering av APIM Developer Portal for AI-API-dokumentasjon.",
          "subtopics": [
            "Portal customization",
            "API documentation",
            "Interactive test console",
            "API key management",
            "User subscription workflow"
          ]
        },
        {
          "id": "versioning-ai-api-endpoints",
          "title": "API Versioning Strategies for AI Endpoints",
          "description": "Strategi for versjonering av AI-API-endepunkter i APIM.",
          "subtopics": [
            "URL vs header versioning",
            "Deprecation timelines",
            "Model version mapping",
            "Migration strategies",
            "Breaking change management"
          ]
        },
        {
          "id": "security-hardening-ai-gateway",
          "title": "Security Hardening for AI Gateways in APIM",
          "description": "Sikkerhetstiltak for AI-gateways i APIM.",
          "subtopics": [
            "IP whitelisting and filtering",
            "Prompt injection prevention",
            "PII detection and masking",
            "Mutual TLS",
            "Audit trail requirements"
          ]
        }
      ]
    },
    "hybrid-edge": {
      "name": "Hybrid Cloud & Edge AI",
      "dir": "hybrid-edge",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "azure-arc-ai-management",
          "title": "Azure Arc for AI Management",
          "description": "Sentralisert administrasjon av AI-arbeidsmengder på tvers av hybrid-miljøer med Azure Arc.",
          "subtopics": [
            "Arc-enabled Kubernetes clusters",
            "Centralized ML model management",
            "Policy and compliance enforcement",
            "Multi-cluster AI governance"
          ]
        },
        {
          "id": "azure-local-ai-workloads",
          "title": "Azure Local for Edge AI Workloads",
          "description": "Implementering av Azure Local for lokal AI-inferencing og ML-pipeline-kjøring.",
          "subtopics": [
            "Cluster-felles ML stack",
            "Local Azure Services",
            "Storage-optimized inferencing",
            "Hybrid resilience patterns"
          ]
        },
        {
          "id": "edge-ai-inferencing-patterns",
          "title": "Edge AI Inferencing Patterns",
          "description": "Arkitekturmønstre for real-time inferencing ved nettverkskanten.",
          "subtopics": [
            "Model quantization and compression",
            "Real-time inference acceleration",
            "Caching patterns for edge",
            "Batching vs streaming inference"
          ]
        },
        {
          "id": "disconnected-ai-scenarios",
          "title": "Disconnected AI Scenarios",
          "description": "AI-løsninger for offline eller intermittent-tilkoblede miljøer.",
          "subtopics": [
            "Offline model deployment",
            "Data reconciliation strategies",
            "Local cache and sync",
            "Fallback inference patterns"
          ]
        },
        {
          "id": "data-sovereignty-norway-public-sector",
          "title": "Data Sovereignty for Norwegian Public Sector",
          "description": "Sikring av datatilgang, lagring og prosessering innenfor norske grenser.",
          "subtopics": [
            "Geographic data residency",
            "Regulatory compliance matrix",
            "Data classification per sector",
            "Cross-border restriction patterns"
          ]
        },
        {
          "id": "iot-operations-ai-integration",
          "title": "IoT Operations and AI Integration",
          "description": "Integrasjon av Azure IoT Operations med AI-inferencing for felt-datainsamling.",
          "subtopics": [
            "Sensor data normalization",
            "Edge gateway AI preprocessing",
            "Time-series analytics at edge",
            "Device-to-cloud AI pipelines"
          ]
        },
        {
          "id": "hybrid-rag-architecture",
          "title": "Hybrid RAG Architecture",
          "description": "RAG for hybrid-miljøer med delt datasøk mellom lokale og cloud-baserte vektordatabaser.",
          "subtopics": [
            "Local embedding and retrieval",
            "Federated vector search",
            "Chunking strategies for split data",
            "Context optimization across tiers"
          ]
        },
        {
          "id": "on-premises-slm-phi-deployment",
          "title": "On-Premises SLM and Phi Model Deployment",
          "description": "Implementering av små språkmodeller og Phi-modeller lokalt.",
          "subtopics": [
            "Phi-3/Phi-4 deployment",
            "Resource-constrained sizing",
            "Prompt optimization for SLM",
            "Fine-tuning at edge"
          ]
        },
        {
          "id": "azure-confidential-computing-ai",
          "title": "Azure Confidential Computing for AI",
          "description": "Bruk av Intel SGX og AMD SEV-SNP for kryptert AI-inferencing.",
          "subtopics": [
            "TEE-enabled model execution",
            "Encrypted inference pipelines",
            "Attestation for compliance",
            "Performance trade-offs"
          ]
        },
        {
          "id": "sovereign-cloud-norway",
          "title": "Sovereign Cloud for Norwegian AI",
          "description": "Isolert sky-infrastruktur for statlig bruk med compliance til NSM-kravene.",
          "subtopics": [
            "Data sovereignty architecture",
            "Regional deployment constraints",
            "Compliance audit trails",
            "Vendor lock-in mitigation"
          ]
        },
        {
          "id": "onnx-runtime-edge-deployment",
          "title": "ONNX Runtime for Edge Deployment",
          "description": "Optimalisering og kjøring av ONNX-modeller på edge-enheter.",
          "subtopics": [
            "ONNX model conversion",
            "Hardware acceleration (GPU/NPU)",
            "Cross-platform compatibility",
            "Performance profiling"
          ]
        },
        {
          "id": "windows-ai-apc-capabilities",
          "title": "Windows AI and AI PC Capabilities",
          "description": "Utnyttelse av Windows AI-rammeverk og NPU-akselerasjon i AI PC-er.",
          "subtopics": [
            "Windows ML og ONNX Runtime",
            "Neural Processing Unit (NPU)",
            "Copilot+ PC specifications",
            "Local LLM inference on device"
          ]
        },
        {
          "id": "azure-iot-hub-ai-pipeline",
          "title": "Azure IoT Hub and AI Pipeline",
          "description": "Integrasjon av IoT Hub med Stream Analytics og Azure ML for sanntidsprosessering.",
          "subtopics": [
            "Device-to-hub data flow",
            "Stream processing for AI",
            "Real-time model scoring",
            "Scaling hybrid ingestion"
          ]
        },
        {
          "id": "kubernetes-edge-aks-edge",
          "title": "Kubernetes-Based AI at the Edge (AKS Edge)",
          "description": "Kjøring av Kubernetes-klynger på edge-enheter med AKS Edge Essentials.",
          "subtopics": [
            "AKS Edge Essentials deployment",
            "Container orchestration at edge",
            "Multi-node edge clusters",
            "Service mesh for edge"
          ]
        },
        {
          "id": "offline-first-ai-applications",
          "title": "Offline-First AI Application Patterns",
          "description": "Applikasjonsmønstre som fungerer offline og synkroniseres når tilkobling etableres.",
          "subtopics": [
            "Local-first data models",
            "Conflict resolution on sync",
            "Progressive enhancement",
            "Offline capability testing"
          ]
        },
        {
          "id": "network-constrained-ai-deployment",
          "title": "Network-Constrained AI Deployment",
          "description": "AI-løsninger optimalisert for lavbåndbredde og høy latency.",
          "subtopics": [
            "Model size reduction",
            "Partial model loading",
            "Bandwidth-aware batching",
            "Latency compensation patterns"
          ]
        },
        {
          "id": "edge-to-cloud-data-synchronization",
          "title": "Edge-to-Cloud Data Synchronization",
          "description": "Pålitelig datasynkronisering mellom edge og cloud med konfliktløsning.",
          "subtopics": [
            "Eventual consistency patterns",
            "Delta sync optimization",
            "Conflict resolution strategies",
            "Data deduplication at scale"
          ]
        },
        {
          "id": "regulatory-compliance-edge-ai",
          "title": "Regulatory Compliance for Edge AI",
          "description": "Oppfyllelse av regulatoriske krav for AI-systemer på lokale nett.",
          "subtopics": [
            "Data protection impact assessment",
            "Risk assessment frameworks",
            "Audit logging at edge",
            "Transparency and explainability"
          ]
        }
      ]
    },
    "multi-modal": {
      "name": "Multi-Modal AI",
      "dir": "multi-modal",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "gpt4o-vision-architecture",
          "title": "GPT-4o Vision Architecture and Capabilities",
          "description": "Detaljert gjennomgang av GPT-4o sin vision-kapabilitet, arkitektur og brukstilfeller.",
          "subtopics": [
            "GPT-4o vision capabilities and token limits",
            "Image input types and preprocessing",
            "Native vs. external vision integration",
            "Performance and latency optimization"
          ]
        },
        {
          "id": "azure-video-indexer-patterns",
          "title": "Azure Video Indexer for Enterprise AI",
          "description": "Bruk av Azure Video Indexer for automatisert videoanalyse og kunnskapsutvinning.",
          "subtopics": [
            "Video ingestion and processing workflows",
            "Face, speech, and content detection",
            "Knowledge graph construction from video",
            "Integration with AI services"
          ]
        },
        {
          "id": "multimodal-rag-architecture",
          "title": "Multi-Modal RAG Architecture Patterns",
          "description": "Design av RAG-systemer som kombinerer tekst, bilder og video.",
          "subtopics": [
            "Multi-modal embedding models",
            "Chunking strategies for images and video",
            "Vector store design for mixed media",
            "Retrieval and ranking patterns"
          ]
        },
        {
          "id": "speech-to-ai-pipelines",
          "title": "Speech-to-AI Integration Pipelines",
          "description": "End-to-end arkitektur for tale-baserte input som integrerer Azure Speech Services med AI-modeller.",
          "subtopics": [
            "Speech recognition and language detection",
            "Audio preprocessing and quality assessment",
            "Low-latency streaming architectures",
            "Error handling and confidence scoring"
          ]
        },
        {
          "id": "dalle-image-generation",
          "title": "DALL-E Image Generation for Public Sector",
          "description": "Bruk av DALL-E via Azure OpenAI for generering av visuelt innhold.",
          "subtopics": [
            "DALL-E 3 capabilities and limitations",
            "Prompt engineering for consistent outputs",
            "Content moderation and safety",
            "Integration with document generation pipelines"
          ]
        },
        {
          "id": "document-vision-processing",
          "title": "Document Intelligence and Vision Processing",
          "description": "Automatisert behandling av dokumenter med skanning, OCR og strukturert utvinning.",
          "subtopics": [
            "Document layout analysis",
            "Table and form extraction",
            "Handwriting recognition",
            "Pre- and post-processing workflows"
          ]
        },
        {
          "id": "accessibility-multimodal-ai",
          "title": "Accessibility in Multi-Modal AI Systems",
          "description": "Utforming av inkluderende AI-løsninger som støtter alle brukertyper.",
          "subtopics": [
            "Alt text generation and WCAG compliance",
            "Audio descriptions for visual content",
            "Caption and transcript generation",
            "User preference and assistive technology integration"
          ]
        },
        {
          "id": "real-time-audio-api",
          "title": "Real-Time Audio API for Conversational AI",
          "description": "Implementering av Azure OpenAI Real-Time Audio API for lav-latency tale-basert interaksjon.",
          "subtopics": [
            "Session management and state tracking",
            "Audio codec selection and bandwidth optimization",
            "Interruption and turn-taking handling",
            "Deployment and scaling patterns"
          ]
        },
        {
          "id": "video-analysis-patterns",
          "title": "Video Analysis and Understanding Patterns",
          "description": "Strategier for å analysere videoinnhold med kombinasjonen av Video Indexer og LLM-modeller.",
          "subtopics": [
            "Scene and action detection",
            "Temporal understanding and summarization",
            "Multi-frame analysis strategies",
            "Integration with narrative understanding"
          ]
        },
        {
          "id": "multimodal-evaluation-metrics",
          "title": "Multi-Modal AI Evaluation and Metrics",
          "description": "Måling av kvalitet og ytelse i multi-modale systemer.",
          "subtopics": [
            "Text generation metrics (BLEU, ROUGE, BERTScore)",
            "Image quality and relevance metrics",
            "Cross-modal alignment measurement",
            "User satisfaction and business KPIs"
          ]
        },
        {
          "id": "cv-llm-integration",
          "title": "Computer Vision and LLM Integration",
          "description": "Kombinasjon av spesialiserte computer vision-modeller med generative LLM-er.",
          "subtopics": [
            "Vision encoder selection and fine-tuning",
            "Prompt injection for visual grounding",
            "Scene understanding and spatial reasoning",
            "Few-shot learning with visual examples"
          ]
        },
        {
          "id": "whisper-speech-recognition",
          "title": "Whisper ASR and Advanced Speech Recognition",
          "description": "Bruk av OpenAI Whisper-modeller for robust talegjenkjenning.",
          "subtopics": [
            "Whisper model selection (tiny to large)",
            "Multi-lingual and Norwegian support",
            "Speaker diarization and identification",
            "Custom vocabularies and fine-tuning"
          ]
        },
        {
          "id": "text-to-speech-citizen",
          "title": "Text-to-Speech for Citizen Services",
          "description": "Implementering av Azure Speech Services TTS for tilgjengelig digital kommunikasjon.",
          "subtopics": [
            "Neural voice selection and customization",
            "SSML markup for prosody control",
            "Multi-lingual citizen support",
            "Performance and cost optimization"
          ]
        },
        {
          "id": "image-classification-understanding",
          "title": "Image Classification and Understanding",
          "description": "Klassifisering og annotasjon av bilder ved hjelp av Azure Computer Vision og LLM-er.",
          "subtopics": [
            "Pre-trained model selection",
            "Custom model training and evaluation",
            "Confidence and uncertainty quantification",
            "Real-time and batch processing"
          ]
        },
        {
          "id": "multimodal-content-safety",
          "title": "Multi-Modal Content Safety and Moderation",
          "description": "Implementering av sikkerhetsbarrierer for tekst, bilder, video og lyd.",
          "subtopics": [
            "Text, image, and audio harm categories",
            "Multi-modal prompt injection detection",
            "Bias detection across modalities",
            "Regulatory compliance and audit logging"
          ]
        },
        {
          "id": "ocr-pipeline-architecture",
          "title": "OCR Pipelines and Text Extraction Architecture",
          "description": "Sluttpunkt-til-slutt arkitektur for optisk tegngjenkjenning.",
          "subtopics": [
            "Image preprocessing and quality assessment",
            "OCR engine selection and configuration",
            "Text normalization and correction",
            "Integration with document understanding"
          ]
        },
        {
          "id": "multimodal-prompt-engineering",
          "title": "Multi-Modal Prompt Engineering Techniques",
          "description": "Teknikker for å skrive effektive prompts som kombinerer tekst og bilder.",
          "subtopics": [
            "Visual grounding and spatial reasoning in prompts",
            "Few-shot examples with images",
            "Chain-of-thought reasoning with visuals",
            "System messages for multi-modal tasks"
          ]
        },
        {
          "id": "audio-video-transcription-workflow",
          "title": "Audio and Video Transcription Workflow Architecture",
          "description": "Automatiserte workflows for transkribering og oversettelse av lyd- og videoinnhold.",
          "subtopics": [
            "Batch transcription at scale",
            "Speaker attribution and diarization",
            "Automatic translation with context preservation",
            "Quality assurance and human-in-the-loop workflows"
          ]
        }
      ]
    },
    "performance-scalability": {
      "name": "Performance & Scalability",
      "dir": "performance-scalability",
      "priority": "MEDIUM",
      "skills": [
        {
          "id": "latency-optimization-azure-openai",
          "title": "Latency Optimization for Azure OpenAI",
          "description": "Strategier for å redusere responstid i Azure OpenAI API-kall.",
          "subtopics": [
            "Request pipeline optimization",
            "Connection pooling and reuse",
            "Regional endpoint selection",
            "Time-to-first-token reduction"
          ]
        },
        {
          "id": "streaming-response-patterns",
          "title": "Streaming Response Patterns",
          "description": "Implementering av streaming-responses for progressiv data-levering.",
          "subtopics": [
            "Server-sent events (SSE)",
            "Chunked transfer encoding",
            "Client-side stream handling",
            "Error recovery in streams"
          ]
        },
        {
          "id": "batch-api-usage-optimization",
          "title": "Batch API Usage and Optimization",
          "description": "Batch-APIets arkitektur og beste praksis for masseprosessering.",
          "subtopics": [
            "Batch job composition",
            "File upload and management",
            "Cost savings calculations",
            "Retry and error handling"
          ]
        },
        {
          "id": "auto-scaling-ai-infrastructure",
          "title": "Auto-Scaling AI Infrastructure",
          "description": "Implementering av dynamisk skalering for AI-arbeidsbelastninger.",
          "subtopics": [
            "Scaling metrics and triggers",
            "Cooldown periods and stabilization",
            "Capacity planning",
            "Cost optimization through scaling"
          ]
        },
        {
          "id": "cdn-edge-caching-ai",
          "title": "CDN and Edge Caching for AI Workloads",
          "description": "Bruk av Azure Front Door og CDN for å cache AI-responses.",
          "subtopics": [
            "Cache-key strategies for AI",
            "Cache invalidation patterns",
            "Geographic distribution",
            "Origin offload benefits"
          ]
        },
        {
          "id": "connection-pooling-patterns",
          "title": "Connection Pooling Patterns",
          "description": "Implementering av connection pooling for HTTP-klienter mot Azure AI Services.",
          "subtopics": [
            "Pool sizing strategies",
            "Keep-alive configuration",
            "Connection recycling",
            "Load distribution"
          ]
        },
        {
          "id": "throughput-optimization-strategies",
          "title": "Throughput Optimization Strategies",
          "description": "Teknikker for å maksimere antall fullførte requests per sekund.",
          "subtopics": [
            "Parallel request execution",
            "Request buffering strategies",
            "Queue depth tuning",
            "System bottleneck identification"
          ]
        },
        {
          "id": "model-distillation-performance",
          "title": "Model Distillation for Performance",
          "description": "Bruk av destillerte modeller for akseptabel nøyaktighet med lavere latens.",
          "subtopics": [
            "Distillation training process",
            "Model size vs. quality tradeoffs",
            "Token reduction benefits",
            "Use case suitability"
          ]
        },
        {
          "id": "async-processing-patterns",
          "title": "Asynchronous Processing Patterns",
          "description": "Design-mønstre for dekoblet prosessering av AI-arbeidsbelastninger.",
          "subtopics": [
            "Queue-based architectures",
            "Event-driven design",
            "Request-response decoupling",
            "Status polling and webhooks"
          ]
        },
        {
          "id": "load-testing-ai-services",
          "title": "Load Testing AI Services",
          "description": "Strategi og verktøy for å teste Azure AI Services under realistiske lastforhold.",
          "subtopics": [
            "Load test design",
            "Realistic traffic patterns",
            "Bottleneck analysis",
            "Capacity forecasting"
          ]
        },
        {
          "id": "token-per-second-optimization",
          "title": "Token-Per-Second Optimization",
          "description": "Teknikker for å maksimere tokens generert per sekund.",
          "subtopics": [
            "Batch sizing impact",
            "Prompt length optimization",
            "GPU utilization",
            "Throughput monitoring"
          ]
        },
        {
          "id": "gpu-compute-sizing",
          "title": "GPU and Compute Sizing for AI",
          "description": "Metodikk for å velge riktig GPU og compute-ressurser.",
          "subtopics": [
            "GPU type comparison",
            "Memory requirements",
            "Batch size influence",
            "Cost-performance analysis"
          ]
        },
        {
          "id": "prompt-caching-performance",
          "title": "Prompt Caching for Performance",
          "description": "Implementering av Azure OpenAI prompt-caching for å eliminere redundant prosessering.",
          "subtopics": [
            "Cache eligibility requirements",
            "Prefix strategy design",
            "Cost reduction calculation",
            "Cache invalidation"
          ]
        },
        {
          "id": "rate-limit-management",
          "title": "Rate Limit Management",
          "description": "Strategier for å håndtere Azure AI Services rate limits.",
          "subtopics": [
            "Exponential backoff implementation",
            "Quota request process",
            "Multi-region failover",
            "Usage monitoring"
          ]
        },
        {
          "id": "concurrent-request-optimization",
          "title": "Concurrent Request Optimization",
          "description": "Design-mønstre for å maksimere antall samtidige requests.",
          "subtopics": [
            "Concurrency level tuning",
            "Request queueing strategies",
            "Deadlock prevention",
            "Resource contention resolution"
          ]
        },
        {
          "id": "regional-deployment-latency",
          "title": "Regional Deployment for Latency Reduction",
          "description": "Multi-region deployment-strategier for Azure AI Services.",
          "subtopics": [
            "Region selection criteria",
            "Traffic routing strategies",
            "Cross-region redundancy",
            "Data residency requirements"
          ]
        },
        {
          "id": "response-chunking-strategies",
          "title": "Response Chunking Strategies",
          "description": "Teknikker for å fragmentere store responses fra AI-modeller.",
          "subtopics": [
            "Semantic chunking approaches",
            "Token boundary alignment",
            "Client-side reassembly",
            "Error handling in chunks"
          ]
        },
        {
          "id": "performance-benchmarking-frameworks",
          "title": "Performance Benchmarking Frameworks",
          "description": "Etablering av benchmarking-rammer for konsistent måling av ytelse.",
          "subtopics": [
            "Metric definition standards",
            "Baseline establishment",
            "Regression detection",
            "Comparative analysis methods"
          ]
        }
      ]
    }
  }
}
