{ "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." } } }