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

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

skills/ms-ai-engineering/references/rag-architecture/rag-caching-optimization.md

@@ -1,6 +1,6 @@
 # RAG Caching and Performance Optimization
 
-**Last updated:** 2026-04 | Verified: MCP 2026-04
+**Last updated:** 2026-06-19 | Verified: MCP 2026-06-19
 **Status:** GA
 **Category:** RAG Architecture & Semantic Search
 
@@ -147,7 +147,7 @@ User Query → L1: Redis (in-memory, <5ms) → L2: Cosmos DB (persistent, <50ms)
 |-------------|---------|---------|
 | **Caching user-private data globally** | Privacy violation, data leakage | Scope cache keys by user/tenant identity |
 | **Ingen TTL policy** | Runaway cache growth, stale data | Implement TTL basert på data sensitivity |
-| **For høy similarity threshold (>0.8)** | Lav cache hit rate | Start med 0.15-0.3, tune basert på metrics |
+| **For lav APIM score-threshold (distanse, f.eks. <0.1)** | Lav cache hit rate (for streng matching) | Start med 0.15, tune opp basert på metrics |
 | **Caching uten context window** | Contextually incorrect responses | Vectorize chat history + latest prompt |
 | **Ingen invalidation strategy** | Stale responses ved data updates | Implement webhook-based invalidation |
 
@@ -236,10 +236,10 @@ Outbound (cache store):
 <azure-openai-semantic-cache-store duration="60" />
 ```
 
-**Score Threshold Tuning:**
-- 0.1-0.2 → Liberal matching, høy hit rate, noe lavere relevance
+**Score Threshold Tuning** (APIM `score-threshold` er en DISTANSE: lavere = strengere, krever høyere semantisk likhet):
+- 0.1-0.2 → Strict matching, lavere hit rate, høy relevance
 - 0.3-0.5 → Balanced, medium hit rate, god relevance
-- 0.6-0.8 → Strict matching, lav hit rate, høy relevance
+- 0.6-0.8 → Liberal matching, høyere hit rate, noe lavere relevance
 
 **Verified** (Microsoft Learn - Enable semantic caching for LLM APIs)
 
@@ -416,7 +416,7 @@ cache_key = f"user:{user_id}:tenant:{tenant_id}:query_hash:{hash(prompt)}"
 |-----------|--------|------------|
 | **Caching uten context window** | Contextually incorrect responses → user frustration | Vectorize chat history + prompt |
 | **Global caching av persondata** | GDPR violation, potential bøter | User-scoped keys, TTL enforcement |
-| **For høy similarity threshold** | Lav hit rate, caching ineffective | Start lavt (0.15), tune opp |
+| **For lav APIM score-threshold (distanse)** | Lav hit rate, caching ineffective | Start på 0.15, tune opp for løsere matching |
 | **Ingen invalidation strategy** | Stale data → incorrect LLM responses | Webhook-based invalidation |
 | **Undersized cache tier** | High eviction rate, lav hit rate | Monitor evictions, scale proaktivt |
 | **Ignoring embedding overhead** | Latency increase vs direct LLM call | Batch embeddings, use async patterns |
@@ -502,7 +502,7 @@ cache_key = f"user:{user_id}:tenant:{tenant_id}:query_hash:{hash(prompt)}"
 
 **Totalt antall kilder:** 9 unike Microsoft Learn URLer
 **MCP calls:** 6 (4 docs_search + 2 docs_fetch + 1 code_sample_search)
-**Sist verifisert:** 2026-02-03
+**Sist verifisert:** 2026-06-19
 
 
 ### Azure Managed Redis — Arkitektur (oppdatert 2026-04)