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chore(ms-ai-architect): refresh KB medium-bucket — 74 files [skip-docs]

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KB-currency refresh (medium priority, 2026-06-19) via /architect:kb-update.
74 medium-prioritets filer re-verifisert mot Microsoft Learn (MCP) — delegert
til 15 parallelle Opus-subagenter (3 bølger) gruppert etter delt kilde, med
disjunkte fil-sett. Verifisert i hovedkontekst (scope-sjekk + diff-review av
de faktatunge gruppene + tester).

Hovedendringer (faktuelle korreksjoner + currency):
- Azure AI Search semantic ranker: TILGJENGELIG PÅ ALLE TIERS (også Free/Basic
  m/ gratis månedlig kvote) — gammel KB sa feilaktig "kun S1+". Korrigert i
  tier-tabell, anti-patterns og beslutningstabell (azure-ai-search-setup).
- APIM score-threshold = DISTANSE (lavere = strengere): tuning-tabellen i
  rag-caching-optimization hadde retningen baklengs — invertert til korrekt.
- Agentic retrieval GA/preview-nyanse presisert (hovedkontekst-korreksjon mot
  agentic-retrieval-how-to-migrate): GA via REST 2026-04-01 returnerer EKSTRAKTIV
  grounding (references + activity), IKKE syntetiserte svar. Answer synthesis,
  ikke-minimal reasoning effort (LLM query planning) og multi-turn messages
  forblir preview (2026-05-01-preview). Subagent hadde overforenklet til "hele
  kjernepipelinen GA"; rettet i agentic-rag-patterns + citation-tracking.
- Copilot Studio modell-tabeller (platforms/copilot-studio): fjernet Claude Opus
  4.5 + GPT-5.2 (borte fra kilde), lagt til Claude Sonnet 4.6/Opus 4.6 (GA),
  Opus 4.7 + Mistral Medium 3.5 (experimental); GPT-5 Reasoning/Auto = preview;
  A2A GA (apr 2026).
- Computer Use (CUA): Copilot Studio GA 2026-05-07; 4 modeller m/ tier/status
  (OpenAI CUA + Sonnet 4.5 GA, Sonnet 4.6 + Opus 4.6 experimental); 5 credits/
  steg standard, 15 premium; US-only region-krav FJERNET i GA-dok; Cloud PC pool
  + Hosted browser + bring-your-own-machine.
- Azure AI Search REST API-versjoner bumpet: 2025-09-01 -> 2026-04-01 (stabil),
  2025-11-01-preview -> 2026-05-01-preview (hybrid-search, rag-security-rbac,
  chunking).
- Power Automate-integrasjon: trigger "Run a flow from Copilot" -> "When an agent
  calls the flow"; App Service innebygd MCP (preview) lagt til.
- M365 Copilot-manifest v1.26 -> v1.28 (GA, mai) / v1.29 dokumentert (juni);
  "Tenant graph grounding" -> "Work IQ".
- Speech fast transcription 2t/300MB -> 5t/500MB; multilingual 14 -> 15 locales
  (+ pt-BR). Content Understanding reasoning preview -> GA (v1.0, 2025-11-01).
- Security Copilot E5 -> E5+E7. Død Databricks-URL ci-cd/best-practices ->
  ci-cd/flows. Prompt Flow retirement (2027-04-20 -> MAF) notert der den
  presenteres som go-forward. Gateway-topologi-tabell-feil rettet.
- Alle 74 Last updated -> 2026-06-19.

Discovery ikke kjørt (historisk kun Databricks-støy) -> 389-telling uendret,
ingen resync. validate 239 PASS, kb-integrity 115/115 (262 orphan-warnings
uendret), gitleaks clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01REiKFhP4w6xGXXqWKpPCJJ
This commit is contained in:
Kjell Tore Guttormsen 2026-06-19 14:02:18 +02:00
commit 070141f06b
74 changed files with 403 additions and 384 deletions
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8
skills/ms-ai-advisor/references/prompt-engineering/adversarial-prompting-and-jailbreaks.md
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@ -1,6 +1,6 @@
# Adversarial Prompting and Security Testing
**Last updated:** 2026-02
**Last updated:** 2026-06-19
**Status:** GA
**Category:** Prompt Engineering & LLM Optimization
@ -757,7 +757,7 @@ Start: AI system security design
2. **Azure Security Benchmark — AI Security:**
https://learn.microsoft.com/en-us/security/benchmark/azure/mcsb-v2-artificial-intelligence-security
*Verifisert: januar 2026, omfatter AI-1 til AI-7 controls*
*Re-verifisert: MCP 2026-06-19, omfatter AI-1 til AI-7 controls (AI-1 godkjente modeller, AI-2 flerlags innholdsfiltrering, AI-3 safety meta-prompts, AI-4 minste privilegium for agentfunksjoner, AI-5 human-in-the-loop, AI-6 monitorering og deteksjon, AI-7 kontinuerlig AI red teaming)*
3. **Azure AI Red Teaming Agent:**
https://learn.microsoft.com/en-us/azure/foundry/concepts/ai-red-teaming-agent
@ -769,14 +769,14 @@ Start: AI system security design
5. **Microsoft Defender for AI Services:**
https://learn.microsoft.com/en-us/azure/defender-for-cloud/ai-threat-protection
*Verifisert: januar 2026, GA*
*Re-verifisert: MCP 2026-06-19, GA. Defender XDR-integrasjon; 30-dagers gratis prøveperiode (cap 75 mrd. tokens); kun tekst-tokens skannes (ikke bilde/lyd)*
**Confidence markers:**
- ✅ **High confidence:** GA features, verifisert mot microsoft.com/learn
- ⚠️ **Medium confidence:** Public Preview features (Azure AI Red Teaming Agent)
- 📘 **Best practice:** Microsoft Security Benchmark (MCSB v2.0)
**Sist oppdatert:** 2026-02-04
**Sist oppdatert:** 2026-06-19
**API versjon (Content Safety):** `2024-03-01-preview` eller nyere
**SDK versjon (PyRIT):** Henviser til Azure/PyRIT GitHub repository

11
skills/ms-ai-advisor/references/prompt-engineering/error-handling-and-fallback-prompting.md
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@ -1,6 +1,6 @@
# Error Handling and Fallback Prompting Strategies
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Last updated:** 2026-06-19 | Verified: MCP 2026-06-19
**Status:** GA
**Category:** Prompt Engineering & LLM Optimization
@ -374,11 +374,12 @@ APIM kan enforces content safety checks automatisk:
</policies>
```
**Policy-attributter (Verified MCP 2026-04):**
**Policy-attributter (Verified MCP 2026-06-19):**
- `backend-id`: Azure AI Content Safety backend i APIM
- `shield-prompt`: Sjekk for brukerangrep/adversarial prompts (true/false)
- `enforce-on-completions`: Aktiver content safety på responser i tillegg til requests
- `window-size`: Tegn per vindu for evaluering (maks 10 000 tegn, konfigurerbart for responser)
- `window-size`: Tegn per vindu for evaluering (default/maks 10 000 tegn; konfigurerbart kun for responser — for requests brukes alltid default)
- `window-overlap-size`: Overlapp i tegn mellom vinduer når innhold splittes etter `window-size` (ingen overlapp hvis uoppgitt)
- `output-type`: FourSeverityLevels (0,2,4,6) eller EightSeverityLevels (0-7)
- Threshold 0 = mest restriktivt, 7 = minst restriktivt. Threshold 4 blokkerer nivå 4-7, tillater 0-3.
- Støtter også `blocklists` for tilpassede ord/uttrykk
@ -690,7 +691,7 @@ User Request
2. [Architecture strategies for self-preservation](https://learn.microsoft.com/en-us/azure/well-architected/reliability/self-preservation) – Azure Well-Architected Framework reliability-mønstre
3. [Azure OpenAI Priority-Based Load Balancer (GitHub)](https://github.com/Azure-Samples/openai-aca-lb) – Referanseimplementasjon av smart load balancing
4. [Troubleshooting Azure OpenAI On Your Data](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/on-your-data-best-practices) – Best practices for debugging og error handling
5. [llm-content-safety policy (APIM)](https://learn.microsoft.com/en-us/azure/api-management/llm-content-safety-policy) (Re-verified MCP 2026-04) – Content safety enforcement i API Management. Policy-attributter: backend-id, shield-prompt, enforce-on-completions, window-size, output-type, threshold (0-7), blocklists.
5. [llm-content-safety policy (APIM)](https://learn.microsoft.com/en-us/azure/api-management/llm-content-safety-policy) (Re-verified MCP 2026-06-19) – Content safety enforcement i API Management. Policy-attributter: backend-id, shield-prompt, enforce-on-completions, window-size, window-overlap-size, output-type, threshold (0-7), blocklists.
**Sekundærkilder:**
6. [Azure OpenAI FAQ](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/faq) – Vanlige feilsituasjoner og workarounds
@ -700,7 +701,7 @@ User Request
**Verifisert:** Alle tekniske detaljer er hentet fra offisielle Microsoft-kilder (learn.microsoft.com, GitHub samples). Kodeeksempler er basert på offisielle SDK-dokumentasjon (januar 2026).
**Confidence markers:**
- **Høy confidence:** HTTP error codes, SDK retry defaults, `Retry-After` header, content safety policies (re-verified MCP 2026-04)
- **Høy confidence:** HTTP error codes, SDK retry defaults, `Retry-After` header, content safety policies (re-verified MCP 2026-06-19)
- **Medium confidence:** Kostnadsestimater (prisene kan variere), spesifikke PTU-priser for norske kunder
- **Lav confidence:** N/A – alle anbefalinger er basert på etablerte mønstre

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skills/ms-ai-advisor/references/prompt-engineering/multimodal-prompt-design.md
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@ -1,6 +1,6 @@
# Multimodal Prompt Design with Images and Text
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Last updated:** 2026-06-19 | Verified: MCP 2026-06-19
**Status:** GA
**Category:** Prompt Engineering & LLM Optimization
@ -187,12 +187,12 @@ messages = [
| Verbalization | Semantisk dybde, LLM-sitérbare beskrivelser | LLM-kall per bilde, høyere latency | Diagrammer, flowcharts, infografikk |
| Direct embeddings | Rask, ingen LLM-kall ved indexing | Ingen forklaring av relasjoner | Visual similarity, produktsøk |
**Azure AI Search multimodal pipeline (Verified MCP 2026-04):**
1. **Content extraction** — velg mellom:
- Document Extraction skill: rask prototyping, PDF-støtte
- Document Layout skill: presise sidetall, bounding boxes, RAG-optimalisert
- Azure Content Understanding skill: avansert — cross-page tabeller, semantisk chunking, DOCX/XLSX/PPTX
2. **Text chunking:** Text Split skill
**Azure AI Search multimodal pipeline (Verified MCP 2026-06-19):**
1. **Content extraction** — to anbefalte innebygde skills:
- Document Extraction skill: rask prototyping/produksjon der eksakt posisjon ikke kreves; bilde-posisjonsmetadata kun for PDF; ingen innebygd chunking (bruk Text Split skill)
- Azure Content Understanding skill: avansert — cross-page tabeller, semantisk chunking (innebygd), AI-genererte bildebeskrivelser, og tekst-/bilde-posisjonsmetadata for PDF, DOCX, XLSX, PPTX
- (Document Layout skill er fortsatt støttet for *eksisterende* pipelines, men for nye skillsets anbefaler Microsoft Azure Content Understanding skill, som slår sammen ekstraksjon og chunking i én skill.)
2. **Text chunking:** Text Split skill (ikke nødvendig med Content Understanding, som chunker semantisk)
3. **Image verbalization:** GenAI Prompt skill + LLM (phi-4, gpt-4o, gpt-5) → naturlig-språklig beskrivelse
4. **Embedding:** Azure OpenAI / Microsoft Foundry / Azure Vision multimodal embeddings
5. **Knowledge store:** Lagrer bilder for retrieval; image-lokasjon lagres i indeks for sitert visning
@ -314,7 +314,7 @@ Can you tell me what the image depicts?
**Pipeline-steg (wizard):**
1. Data source: Azure Blob / ADLS Gen2
2. Content extraction: Document Extraction / Layout / Content Understanding skill
2. Content extraction: Document Extraction skill eller Azure Content Understanding skill (Document Layout skill kun for eksisterende pipelines)
3. Text chunking: Text Split skill
4. Image verbalization (optional): GenAI Prompt skill
5. Embedding: Azure OpenAI / Foundry / Azure Vision
@ -457,7 +457,7 @@ Multimodal scenario?
│ └─ Azure AI Search multimodal RAG (verbalization eller direct embeddings)
│
└─ RAG over PDF/Office-dokumenter med embedded diagrammer?
├─ Forklaringsrike visuals: Document Layout skill + GenAI Prompt verbalization
├─ Forklaringsrike visuals: Azure Content Understanding skill (eller Document Extraction) + GenAI Prompt verbalization
└─ Visual similarity: Azure Content Understanding + Azure Vision embeddings
```
@ -541,13 +541,13 @@ AzureDiagnostics
## Kilder og verifisering
**Microsoft Learn dokumentasjon (verifisert 2026-02):**
**Microsoft Learn dokumentasjon (re-verifisert MCP 2026-06-19):**
- [Use vision-enabled chat models](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/gpt-with-vision) — Offisiell how-to guide for GPT-4o/GPT-4 Turbo with Vision
- [Image prompt engineering techniques](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/gpt-4-v-prompt-engineering) — Best practices for multimodal prompting
- [Multimodal search in Azure AI Search](https://learn.microsoft.com/en-us/azure/search/multimodal-search-overview) (Re-verified MCP 2026-04) — RAG-arkitektur; extraction skill-sammenligning (Document Extraction vs Layout vs Content Understanding); verbalization vs direct embeddings; hybrid query-alternativ
- [Multimodal search in Azure AI Search](https://learn.microsoft.com/en-us/azure/search/multimodal-search-overview) (Re-verified MCP 2026-06-19) — RAG-arkitektur; to anbefalte extraction skills (Document Extraction og Azure Content Understanding; Document Layout kun for eksisterende pipelines); verbalization vs direct embeddings; image-to-vector-queries krever Azure Vision / AML multimodal embeddings-vectorizer; hybrid query-alternativ
- [Azure OpenAI models](https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure) — Modelloversikt og token-kostnader
- [Quickstart: Multimodal search in Azure portal](https://learn.microsoft.com/en-us/azure/search/search-get-started-portal-image-search) — Wizard-basert oppsett
- [Get started with multimodal vision chat apps](https://learn.microsoft.com/en-us/azure/developer/ai/get-started-app-chat-vision) — End-to-end sample app med Base64 encoding
- [Get started with multimodal vision chat apps](https://learn.microsoft.com/en-us/azure/developer/ai/get-started-app-chat-vision) (Re-verified MCP 2026-06-19) — End-to-end sample app: Base64-enkoder opplastet bilde i frontend (FileReader), sender via Azure OpenAI Responses API (`input_image`/`input_text`, default gpt-4o), managed identity-autentisering, deploy til Azure Container Apps
**Code samples:**
- Azure-Samples/cognitive-services-sample-data-files (GitHub)
@ -559,5 +559,5 @@ AzureDiagnostics
- ⚠️ **Medium confidence:** Kostberegninger i NOK (basert på jan 2026 pricing, kan variere)
- ⚠️ **Medium confidence:** Offentlig sektor use cases (inferert fra generelle patterns, ikke Microsoft-spesifikt)
**Sist verifisert:** 2026-04-10
**Neste review:** 2026-07 (eller ved nye GPT-modeller/AI Search features)
**Sist verifisert:** 2026-06-19
**Neste review:** 2026-09-19 (eller ved nye GPT-modeller/AI Search features)

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skills/ms-ai-advisor/references/prompt-engineering/prompt-testing-and-evaluation.md
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@ -1,6 +1,6 @@
# Prompt Testing, Evaluation and Iteration
**Last updated:** 2026-02
**Last updated:** 2026-06-19
**Status:** GA
**Category:** Prompt Engineering & LLM Optimization
@ -71,6 +71,8 @@ Tilgjengelig i regioner som støtter Response API. Genererer testdata basert på
### 2. Prompt Flow Evaluation Framework
> **Utfasing (verifisert MCP 2026-06-19):** Prompt Flow i Microsoft Foundry og Azure Machine Learning utfases **20. april 2027** og anbefales ikke lenger for ny utvikling. Migrer eksisterende Prompt Flow-applikasjoner og -deployments til **Microsoft Agent Framework (MAF)** før denne datoen. Prompt Flow-runtime-images (`promptflow-runtime`, `promptflow-runtime-stable`, `promptflow-python`) får ikke lenger oppdateringer, inkludert sikkerhetsoppdateringer. Evaluation-mønstrene under er fortsatt gyldige for eksisterende løsninger, men velg MAF / Azure AI Evaluation SDK for nye prosjekter.
**Beskrivelse:** SDK-basert evalueringsrammeverk som lar deg bygge custom evaluation flows som Python-kode eller Prompty-filer, kjøre batch evaluations og logge metrics programmatisk.
**Evaluation Flow Lifecycle:**
@ -1045,7 +1047,7 @@ def grade(sample: dict, item: dict) -> float:
**High Confidence (>95%):**
- Built-in evaluators (Groundedness, Relevance, Safety) er production-ready og widely used
- Prompt Flow SDK evaluation workflow er stable (GA since 2023)
- Prompt Flow SDK evaluation workflow er stable (GA siden 2023), men utfases 20. april 2027 — migrer til Microsoft Agent Framework / Azure AI Evaluation SDK for ny utvikling
- Azure AI Foundry Evaluation Portal er GA (as of 2024)
**Medium Confidence (70-95%):**
@ -1064,7 +1066,7 @@ def grade(sample: dict, item: dict) -> float:
**Primary Sources (Microsoft Learn):**
1. [Evaluate generative AI models and applications - Azure AI Foundry](https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluate-generative-ai-app?view=foundry-classic) — GA
2. [Evaluation flows and metrics - Azure Machine Learning Prompt Flow](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2) — GA
2. [Evaluation flows and metrics - Azure Machine Learning Prompt Flow](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2) — GA. Re-verifisert MCP 2026-06-19: Prompt Flow utfases 20. april 2027 → migrer til Microsoft Agent Framework. `log_metric()`/aggregation-mønster uendret.
3. [Azure AI Evaluation SDK - Python API](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-evaluation-readme?view=azure-python) — GA
4. [Agent evaluation with Azure AI Evaluation SDK](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/agent-evaluate-sdk?view=foundry-classic) — GA
@ -1073,6 +1075,6 @@ def grade(sample: dict, item: dict) -> float:
2. [Continuous evaluation setup](https://learn.microsoft.com/en-us/azure/foundry/observability/how-to/how-to-monitor-agents-dashboard?view=foundry)
3. [Custom evaluator registration](https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/cloud-evaluation?view=foundry-classic#specify-custom-evaluators)
**Last Verified:** 2026-02-04
**Version:** Azure AI Foundry v2 (2024-2026), Prompt Flow v1.13+ (2024-2026)
**Last Verified:** 2026-06-19
**Version:** Azure AI Foundry v2 (2024-2026), Prompt Flow v1.13+ (2024-2026; utfases 2027-04-20 → Microsoft Agent Framework)
**MCP Calls:** 3 (microsoft_docs_search × 2, microsoft_docs_fetch × 2, microsoft_code_sample_search × 1)

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skills/ms-ai-advisor/references/prompt-engineering/reasoning-models-o1-o3-optimization.md
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@ -1,6 +1,6 @@
# Reasoning Models (O1/O3) Optimization and Usage
**Last updated:** 2026-02
**Last updated:** 2026-06-19
**Status:** GA
**Category:** Prompt Engineering & LLM Optimization
@ -306,7 +306,7 @@ Reasoning models er tilgjengelige i Azure AI Foundry (tidligere Azure AI Studio)
### Power Platform AI Builder
Fra mars 2025 er O1-modeller tilgjengelige i **Prompt Builder**:
Fra 21. mars 2025 er O1-modeller tilgjengelige i public preview i **Prompt Builder** (release-plan-siden er nå arkivert — se aktuell AI Builder-oversikt for status):
- Velg "o1 reasoning model" i model selector
- Ideell for store datasett-analyse, prognoser, og detaljert analyse
- Kombiner med Power Automate for automatisering av reasoning-oppgaver
@ -511,7 +511,7 @@ Kunder som migrerer fra GPT-4 prøver ofte å bruke "tenk steg-for-steg" teknikk
## Kilder og verifisering
Denne kunnskapsreferansen er basert på offisiell Microsoft Learn-dokumentasjon hentet via MCP (Model Context Protocol) i februar 2026. Alle tekniske detaljer er verifisert mot siste API-versjon.
Denne kunnskapsreferansen er basert på offisiell Microsoft Learn-dokumentasjon hentet via MCP (Model Context Protocol), sist re-verifisert 2026-06-19. Alle tekniske detaljer er verifisert mot siste API-versjon.
**Primary sources:**
@ -537,7 +537,7 @@ Denne kunnskapsreferansen er basert på offisiell Microsoft Learn-dokumentasjon
6. **Power Platform O1 model support**
https://learn.microsoft.com/en-us/power-platform/release-plan/2024wave2/ai-builder/use-o1-reasoning-model-prompt-builder
*Confidence: Verified (MCP search 2026-02)*
*Confidence: Verified (MCP fetch 2026-06-19) — release-plan-siden er arkivert; public preview fra 21. mars 2025*
**Confidence levels per seksjon:**