Retention Policy — Enterprise Knowledge Governance

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WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

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Guardrails and fix patterns for enterprise knowledge retention. Use this page when AI systems over-retain, delete too early, or mix expired data with active knowledge.

When to use this page

AI responses reference documents that should have been deleted per policy.
Retained snippets do not respect jurisdictional time limits (e.g., GDPR 3 years).
Knowledge base or embeddings store does not purge revisions.
RAG answers mix archived with active content.

Core acceptance targets

ΔS(question, expired_snippet) ≥ 0.70 → expired content must not surface.
All snippets carry {expiry_date, retention_scope, audit_hash} fields.
Coverage ≥ 0.70 within active retention window only.
λ remains convergent across three paraphrases and two seeds.

Typical retention problems → exact fix

Symptom	Likely cause	Open this
Expired docs still retrieved	Store never purged embeddings	vectorstore-fragmentation.md
Wrong answer mixes expired + active	Snippets missing `expiry_date` field	data-contracts.md
AI cites “archived only” docs as live	Retrieval trace missing retention scope	retrieval-traceability.md

Fix in 60 seconds

Check ΔS to expired content: run probe with expired snippets, expect ΔS ≥ 0.70.
Schema enforcement: require expiry_date and retention_scope in every snippet.
Index purge: remove expired embeddings before next RAG run.
Audit λ: if λ flips when expired vs active co-exist, clamp with BBAM and enforce contracts.

Copy-paste schema (JSON)

{
  "snippet_id": "KB-5532",
  "expiry_date": "2025-12-31",
  "retention_scope": "eu-3y",
  "audit_hash": "sha256:...",
  "text": "..."
}

Escalate when

Expired content continues to surface after purge.
ΔS < 0.70 against expired content → embeddings contamination.
Audit requires full deletion trace and cannot be reproduced.

Use retrieval-playbook.md for deep purge testing and eval_rag_precision_recall.md to validate coverage.

🔗 Quick-Start Downloads

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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