{
  "version": "1.0",
  "created": "2026-02-03",
  "target_dir": "skills/ms-ai-engineering/references",
  "total_estimated_skills": "300-350",
  "waves": [
    {
      "wave": 1,
      "priority": "HIGH",
      "description": "Kritisk manglende kunnskap for enterprise AI-arkitektur",
      "categories": [
        "azure-ai-services",
        "rag-architecture",
        "responsible-ai",
        "copilot-extensibility",
        "prompt-engineering",
        "cost-optimization",
        "mlops-genaiops"
      ]
    },
    {
      "wave": 1.5,
      "priority": "HIGH",
      "description": "Utredningsstøtte: norsk offentlig sektor, AI-sikkerhet og observerbarhet",
      "categories": [
        "norwegian-public-sector-governance",
        "ai-security-engineering",
        "monitoring-observability"
      ]
    },
    {
      "wave": 2,
      "priority": "MEDIUM",
      "description": "Verdifulle tillegg for komplett arkitekturdekning",
      "categories": [
        "agent-orchestration",
        "bcdr",
        "data-engineering",
        "api-management",
        "hybrid-edge",
        "multi-modal",
        "performance-scalability"
      ]
    }
  ],
  "categories": {
    "azure-ai-services": {
      "name": "Azure AI Services (Foundry Tools)",
      "dir": "azure-ai-services",
      "priority": "HIGH",
      "description": "Pre-bygde AI-tjenester: Vision, Speech, Language, Document Intelligence, Translator, Content Understanding. Fundamentale byggeblokker for enterprise AI.",
      "estimated_skills": 20,
      "examples": [
        "azure-ai-vision-overview",
        "document-intelligence-models",
        "speech-services-architecture",
        "language-services-text-analytics",
        "content-understanding-multimodal",
        "translator-custom-models",
        "azure-ai-search-indexing",
        "custom-vision-vs-florence",
        "ai-services-networking-security",
        "ai-services-pricing-optimization"
      ],
      "existing_overlap": ["platforms/azure-ai-foundry.md"],
      "notes": "Foundry Tools er ny branding (2025). Unngå duplikering med azure-ai-foundry.md som dekker overordnet plattform."
    },
    "rag-architecture": {
      "name": "RAG Architecture & Semantic Search",
      "dir": "rag-architecture",
      "priority": "HIGH",
      "description": "Retrieval-Augmented Generation med Azure AI Search. Vektorindeksering, embedding, hybrid search, reranking, chunking, citation tracking.",
      "estimated_skills": 22,
      "examples": [
        "rag-architecture-patterns",
        "azure-ai-search-vector-indexing",
        "embedding-model-selection",
        "chunking-strategies",
        "hybrid-search-configuration",
        "semantic-ranker-optimization",
        "rag-evaluation-metrics",
        "multi-index-federation",
        "rag-security-rbac",
        "graphrag-knowledge-graphs"
      ],
      "existing_overlap": ["architecture/decision-trees.md"],
      "notes": "RAG er det vanligste mønsteret for enterprise AI. Detaljer er planlagt som ms-rag-architect plugin men grunnleggende arkitektur dekkes her."
    },
    "responsible-ai": {
      "name": "Responsible AI & Governance",
      "dir": "responsible-ai",
      "priority": "HIGH",
      "description": "Microsofts Responsible AI-rammeverk, AI-etikk, bias-deteksjon, forklarbarhet, GDPR/AI Act compliance, AI governance for offentlig sektor.",
      "estimated_skills": 22,
      "examples": [
        "responsible-ai-framework-overview",
        "ai-act-compliance-guide",
        "bias-detection-mitigation",
        "model-explainability-techniques",
        "ai-governance-structure",
        "ai-center-of-excellence",
        "red-teaming-ai-models",
        "content-safety-implementation",
        "ai-impact-assessment",
        "transparency-documentation"
      ],
      "existing_overlap": ["architecture/security.md", "architecture/public-sector-checklist.md"],
      "notes": "Utfyller security.md (teknisk sikkerhet) med governance og compliance. Spesielt viktig for offentlig sektor etter AI Act."
    },
    "copilot-extensibility": {
      "name": "Copilot Extensibility & Integration",
      "dir": "copilot-extensibility",
      "priority": "HIGH",
      "description": "Utvidelse av M365 Copilot og Copilot Studio: declarative agents, custom engine agents, plugins, connectors, Graph API, MCP.",
      "estimated_skills": 22,
      "examples": [
        "declarative-agents-overview",
        "custom-engine-agents",
        "copilot-studio-topics-entities",
        "graph-api-for-copilot",
        "copilot-connectors-patterns",
        "mcp-integration-copilot-studio",
        "copilot-analytics-usage",
        "teams-copilot-extensions",
        "sharepoint-agents",
        "copilot-studio-dlp-governance"
      ],
      "existing_overlap": ["platforms/copilot-studio.md", "platforms/m365-copilot.md"],
      "notes": "Går dypere enn eksisterende plattformfiler. Fokus på implementeringsmønstre, ikke overordnet arkitektur."
    },
    "prompt-engineering": {
      "name": "Prompt Engineering & LLM Optimization",
      "dir": "prompt-engineering",
      "priority": "HIGH",
      "description": "System message design, few-shot/zero-shot teknikker, chain-of-thought, reasoning-modeller (O1/O3), grounding, output-formatering.",
      "estimated_skills": 18,
      "examples": [
        "system-message-design-patterns",
        "few-shot-learning-techniques",
        "chain-of-thought-prompting",
        "reasoning-models-o1-o3",
        "structured-output-json-mode",
        "function-calling-patterns",
        "grounding-with-search",
        "temperature-and-sampling",
        "token-optimization-techniques",
        "prompt-testing-evaluation"
      ],
      "existing_overlap": [],
      "notes": "Helt nytt område. Direkte påvirkning på kvaliteten av alle AI-løsninger."
    },
    "cost-optimization": {
      "name": "Cost Optimization & FinOps for AI",
      "dir": "cost-optimization",
      "priority": "HIGH",
      "description": "Token-optimalisering, caching, reserved capacity, modellvalg, Azure Cost Management, chargeback, budsjettplanlegging for AI.",
      "estimated_skills": 20,
      "examples": [
        "token-cost-optimization",
        "semantic-caching-patterns",
        "reserved-capacity-planning",
        "model-selection-price-performance",
        "azure-cost-management-ai",
        "ptu-vs-paygo-decision",
        "ai-builder-credits-transition",
        "cost-allocation-chargeback",
        "budget-forecasting-ai",
        "small-language-models-cost"
      ],
      "existing_overlap": ["architecture/cost-models.md"],
      "notes": "Utfyller cost-models.md med dypere strategier. cost-models.md dekker prislister, dette dekker optimaliseringsteknikker."
    },
    "mlops-genaiops": {
      "name": "MLOps & GenAIOps",
      "dir": "mlops-genaiops",
      "priority": "HIGH",
      "description": "CI/CD for AI, modellmonitorering, versjonshåndtering, A/B-testing, retraining, evaluering, Azure ML pipelines for produksjon.",
      "estimated_skills": 22,
      "examples": [
        "genaiops-overview",
        "azure-ml-pipelines",
        "model-versioning-registry",
        "llm-evaluation-framework",
        "ab-testing-ai-models",
        "data-drift-monitoring",
        "automated-retraining",
        "ci-cd-ai-models",
        "prompt-flow-production",
        "model-deployment-strategies"
      ],
      "existing_overlap": [],
      "notes": "Helt nytt område. Kritisk for å gå fra prototyp til produksjon."
    },
    "data-engineering": {
      "name": "Data Engineering for AI",
      "dir": "data-engineering",
      "priority": "MEDIUM",
      "description": "Dataintegrasjon med Microsoft Fabric, Data Factory, OneLake, Databricks. Zero-ETL, lakehouse-arkitektur, AI-drevet dataintegrering.",
      "estimated_skills": 22,
      "examples": [
        "microsoft-fabric-for-ai",
        "onelake-data-strategy",
        "data-factory-ai-pipelines",
        "zero-etl-patterns",
        "data-quality-for-ai",
        "real-time-streaming-ai",
        "dataverse-ai-integration",
        "data-lakehouse-architecture",
        "data-governance-purview",
        "synthetic-data-generation"
      ],
      "existing_overlap": [],
      "notes": "Datakvalitet er #1 årsak til AI-prosjektfeil. Microsoft Fabric er raskt voksende."
    },
    "api-management": {
      "name": "API Management & AI Gateway",
      "dir": "api-management",
      "priority": "MEDIUM",
      "description": "Azure API Management som AI-gateway: rate limiting, token quota, load balancing, circuit breaker, autentisering, multi-region.",
      "estimated_skills": 18,
      "examples": [
        "apim-ai-gateway-overview",
        "token-rate-limiting",
        "load-balancing-openai",
        "circuit-breaker-patterns",
        "multi-region-gateway",
        "apim-authentication-patterns",
        "backend-pool-management",
        "streaming-support-apim",
        "cost-tracking-apim",
        "apim-vs-direct-access"
      ],
      "existing_overlap": [],
      "notes": "Viktig for enterprise-skalering. APIM AI Gateway er relativt nytt (2024-2025)."
    },
    "hybrid-edge": {
      "name": "Hybrid Cloud & Edge AI",
      "dir": "hybrid-edge",
      "priority": "MEDIUM",
      "description": "Azure Arc, Azure Local, IoT Operations, edge AI inferencing, disconnected scenarios, datasuverenitet for offentlig sektor.",
      "estimated_skills": 18,
      "examples": [
        "azure-arc-ai-management",
        "azure-local-ai-workloads",
        "edge-ai-inferencing",
        "disconnected-ai-scenarios",
        "data-sovereignty-patterns",
        "iot-operations-ai",
        "hybrid-rag-architecture",
        "on-premises-llm-deployment",
        "azure-confidential-computing",
        "sovereign-cloud-norway"
      ],
      "existing_overlap": [],
      "notes": "Spesielt relevant for norsk offentlig sektor med suverenitetskrav og sikkerhetsgradert informasjon."
    },
    "bcdr": {
      "name": "Business Continuity & Disaster Recovery",
      "dir": "bcdr",
      "priority": "MEDIUM",
      "description": "HA, DR og BCDR for AI: multi-region, backup, failover, RTO/RPO for Azure OpenAI og AI Foundry.",
      "estimated_skills": 16,
      "examples": [
        "multi-region-azure-openai",
        "ai-foundry-dr-planning",
        "backup-recovery-strategies",
        "failover-testing-ai",
        "rto-rpo-ai-services",
        "data-replication-patterns",
        "geo-redundancy-search",
        "incident-response-ai",
        "capacity-planning-dr",
        "compliance-bcdr-requirements"
      ],
      "existing_overlap": [],
      "notes": "Nødvendig for kritiske produksjonssystemer i offentlig sektor."
    },
    "multi-modal": {
      "name": "Multi-Modal AI",
      "dir": "multi-modal",
      "priority": "MEDIUM",
      "description": "Tekst + bilde + tale + video: GPT-4V/GPT-5 vision, Video Indexer, Speech-integrasjon, multi-modal RAG, aksessibilitet.",
      "estimated_skills": 18,
      "examples": [
        "gpt-vision-architecture",
        "video-indexer-ai",
        "multi-modal-rag",
        "speech-to-ai-pipelines",
        "image-generation-dall-e",
        "document-vision-processing",
        "accessibility-multi-modal",
        "real-time-audio-api",
        "video-analysis-patterns",
        "multi-modal-evaluation"
      ],
      "existing_overlap": [],
      "notes": "Økende etterspørsel etter multi-modale løsninger. GPT-5 styrker vision-kapabiliteter."
    },
    "agent-orchestration": {
      "name": "Agent Orchestration & Automation",
      "dir": "agent-orchestration",
      "priority": "MEDIUM",
      "description": "Multi-agent systemer, orkesteringsmønstre, agent-kommunikasjon, Agent 365, Semantic Kernel/Agent Framework-mønstre.",
      "estimated_skills": 20,
      "examples": [
        "multi-agent-patterns",
        "agent-orchestration-topologies",
        "agent-to-agent-communication",
        "agent-365-governance",
        "semantic-kernel-agents",
        "agent-memory-patterns",
        "tool-use-patterns",
        "agent-evaluation-testing",
        "human-in-the-loop-agents",
        "autonomous-workflow-patterns"
      ],
      "existing_overlap": ["development/agent-framework.md"],
      "notes": "Utfyller agent-framework.md med orkestrerings- og designmønstre."
    },
    "performance-scalability": {
      "name": "Performance & Scalability",
      "dir": "performance-scalability",
      "priority": "MEDIUM",
      "description": "Latency-reduksjon, throughput, caching, batching, streaming, auto-scaling, CDN for AI-workloads.",
      "estimated_skills": 18,
      "examples": [
        "latency-optimization-openai",
        "streaming-responses-patterns",
        "batch-api-usage",
        "auto-scaling-ai-infra",
        "cdn-edge-caching-ai",
        "connection-pooling-patterns",
        "throughput-optimization",
        "model-distillation-perf",
        "async-processing-patterns",
        "load-testing-ai-services"
      ],
      "existing_overlap": [],
      "notes": "Viktig for brukeropplevelse. Komplementerer cost-optimization."
    },
    "monitoring-observability": {
      "name": "Monitoring & Observability",
      "dir": "monitoring-observability",
      "priority": "HIGH",
      "description": "Azure Monitor, Application Insights, Log Analytics for AI. Token tracking, anomaly detection, dashboards, alerting.",
      "estimated_skills": 18,
      "examples": [
        "azure-monitor-ai-workloads",
        "application-insights-llm",
        "token-usage-tracking",
        "anomaly-detection-ai",
        "custom-ai-dashboards",
        "alerting-strategies-ai",
        "distributed-tracing-ai",
        "log-analytics-ai-queries",
        "sla-monitoring-ai",
        "cost-attribution-monitoring"
      ],
      "existing_overlap": [],
      "notes": "Nødvendig for produksjonsoperasjoner. Komplementerer MLOps."
    },
    "norwegian-public-sector-governance": {
      "name": "Norwegian Public Sector AI Governance",
      "dir": "norwegian-public-sector-governance",
      "priority": "HIGH",
      "description": "Norsk lovverk, Digdir-rammeverk og forvaltningsmetodikk anvendt på AI. Utredningsinstruksen, Digdirs 7 arkitekturprinsipper, rammeverk for digital samhandling (EIF), DPIA, ROS-analyse, NSM grunnprinsipper, anskaffelser og gevinstrealisering for AI i offentlig sektor.",
      "estimated_skills": 20,
      "research_sources": ["websearch", "regjeringen.no", "lovdata.no", "digdir.no", "nsm.no", "datatilsynet.no"],
      "examples": [
        "utredningsinstruksen-methodology",
        "forvaltningsloven-ai-decisions",
        "digdir-principle-1-user-centric",
        "digdir-principle-4-trust",
        "digital-samhandling-5-layers",
        "dpia-norwegian-methodology",
        "ros-analyse-ai-systems",
        "nsm-grunnprinsipper-ai-mapping",
        "anskaffelser-ai-procurement",
        "gevinstrealisering-ai-projects"
      ],
      "existing_overlap": ["architecture/public-sector-checklist.md", "architecture/ai-utredning-template.md"],
      "notes": "Fundamentalt annerledes enn øvrige kategorier: primærkilder er regjeringen.no, lovdata.no, digdir.no, nsm.no — IKKE microsoft-learn. Innhold er regulatorisk/juridisk, ikke teknisk produktdokumentasjon. Prompt-template må bruke WebSearch for norske kilder i tillegg til microsoft-learn MCP."
    },
    "ai-security-engineering": {
      "name": "AI Security Engineering",
      "dir": "ai-security-engineering",
      "priority": "HIGH",
      "description": "Operasjonell AI-sikkerhet: prompt injection forsvar, jailbreak-prevention, content safety kalibrering, PII-deteksjon, trusselmodellering, sikkerhetsscoring, hendelseshåndtering, output-validering, zero trust for AI, datalekkasjeforebygging og red teaming.",
      "estimated_skills": 15,
      "examples": [
        "prompt-injection-defense-patterns",
        "jailbreak-prevention-production",
        "content-safety-filter-calibration",
        "pii-detection-norwegian-text",
        "ai-threat-modeling-stride",
        "security-scoring-rubric-6dimensions",
        "ai-incident-response-procedures",
        "output-validation-grounding-verification",
        "zero-trust-ai-services",
        "ai-red-team-operations-practical"
      ],
      "existing_overlap": ["architecture/security.md", "responsible-ai/red-teaming-ai-models.md", "responsible-ai/content-safety-implementation.md", "prompt-engineering/adversarial-prompting-and-jailbreaks.md"],
      "notes": "Komplementerer responsible-ai (governance/teori) og prompt-engineering (angrepsteknikker) med OPERASJONELLE forsvarsmønstre. Fokus: forsvar, deteksjon, respons — ikke policy eller angrep."
    }
  }
}
