docs(architect): weekly KB update — 106 files refreshed (2026-04)

Updates across all 5 skills: ms-ai-advisor, ms-ai-engineering,
ms-ai-governance, ms-ai-security, ms-ai-infrastructure.

Key changes:
- Language Services (Custom Text Classification, Text Analytics, QnA):
  retirement warning 2029-03-31, migration guides to Foundry/GPT-4o
- Agentic Retrieval: 50M free reasoning tokens/month (Public Preview)
- Computer Use: Claude Sonnet 4.5 (preview) + OpenAI CUA models
- Agent Registry: Risks column (M365 E7), user-shared/org-published types
- Declarative agents: schema v1.5 → v1.6, Store validation requirements
- MLflow 3: 13 built-in LLM judges, production monitoring, Genie Code
- AG-UI HITL: ApprovalRequiredAIFunction (C#) + @tool(approval_mode) (Python)
- Entra ID Ignite 2025: Agent ID Admin/Developer RBAC roles, Conditional Access
- Security Copilot: 400 SCU/month per 1000 M365 E5 licenses, auto-provisioned
- Fast Transcription API: phrase lists, 14-language multi-lingual transcription
- Azure Monitor Workbooks: Bicep support, RBAC specifics
- Power Platform Copilot: data residency (Norway/Europe → EU DB, Bing → USA)
- RAG security-rbac: 4-approach table (GA + 3 preview access control methods)
- IaC MLOps: Well-Architected OE:05 principles, Bicep/Terraform patterns
- Translator: image file batch translation Preview (JPEG/PNG/BMP/WebP)

All 106 files: Last updated 2026-04 | Verified: MCP 2026-04

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

skills/ms-ai-advisor/references/prompt-engineering/multimodal-prompt-design.md

@@ -1,6 +1,6 @@
 # Multimodal Prompt Design with Images and Text
 
-**Last updated:** 2026-02
+**Last updated:** 2026-04 | Verified: MCP 2026-04
 **Status:** GA
 **Category:** Prompt Engineering & LLM Optimization
 
@@ -187,12 +187,20 @@ 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:**
-1. Document extraction (Document Extraction / Layout / Content Understanding skill)
-2. Text chunking (Text Split skill)
-3. Image verbalization (GenAI Prompt skill + LLM)
-4. Embedding (Azure OpenAI / Foundry / Azure Vision)
-5. Knowledge store (for image storage og retrieval)
+**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
+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
+
+**To retrieval-stier:**
+- Verbalized content → hybrid queries (text + vector). Gir semantisk dybde og LLM-siterbare beskrivelser.
+- Direct multimodal embeddings (Azure Vision) → image-to-vector queries. Effektiv visual similarity uten LLM-kall ved indexing.
+- Mange løsninger kombinerer begge: forklaringsrike visuals verbaliseres, foto/produktbilder embeddes direkte.
 
 ## Beslutningsveiledning
 
@@ -445,8 +453,12 @@ Multimodal scenario?
 ├─ Volum > 10k bilder/dag
 │  └─ Azure AI Search multimodal pipeline + Azure Vision embeddings
 │
-└─ Trengs søk over historiske bilder?
-   └─ Azure AI Search multimodal RAG (verbalization eller direct embeddings)
+├─ Trengs søk over historiske bilder?
+│  └─ 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
+   └─ Visual similarity: Azure Content Understanding + Azure Vision embeddings
 ```
 
 ### Red Flags
@@ -532,7 +544,7 @@ AzureDiagnostics
 **Microsoft Learn dokumentasjon (verifisert 2026-02):**
 - [Use vision-enabled chat models](https://learn.microsoft.com/en-us/azure/ai-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) — RAG-arkitektur med image verbalization og direct embeddings
+- [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
 - [Azure OpenAI models](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models) — 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
@@ -547,5 +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:** 2026-02-04
-**Neste review:** 2026-04 (eller ved nye GPT-modeller)
+**Sist verifisert:** 2026-04-10
+**Neste review:** 2026-07 (eller ved nye GPT-modeller/AI Search features)