Policy Baseline

🧭 Quick Return to Map

You are in a sub-page of Governance.
To reorient, go back here:

• Governance — policy enforcement and compliance controls
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

This page defines the baseline governance policies every AI or RAG pipeline must enforce before scaling.
If policies are missing, unclear, or unenforced, you risk silent drift in outputs, hallucinations re-entering, or compliance violations.
Use these checks to create a structural foundation and verify with measurable acceptance targets.

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When to use this page

• No clear baseline for data usage, model updates, or prompt changes.
• Teams argue over “policy by exception” instead of a shared rulebook.
• Compliance asks for guarantees, but your audit trail cannot prove them.
• Safety or security incidents trigger blame on “undefined responsibilities.”

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Acceptance targets

• Coverage: ≥ 0.95 of datasets, prompts, models, and eval flows mapped to explicit policies.
• Traceability: 100% of policy documents link to lineage and audit logs.
• Enforcement: ΔS(question, retrieved) ≤ 0.45 when querying governed datasets.
• Convergence: λ remains convergent across 3 paraphrases and 2 seeds.
• Expiry: Every waiver or exception tagged with owner and end-date.

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Common policy failures → exact fixes

Symptom	Likely cause	Open this
Datasets used without clarity on rights	license ambiguity or drift	license_and_dataset_rights.md
No control on prompt or instruction drift	missing policy baseline	prompt_policy_and_change_control.md
Model updates shipped silently	lack of release governance	model_governance_model_cards_and_releases.md
Audit asks “who approved this?”	missing sign-off gate	eval_governance_gates_and_signoff.md
Sensitive data leaked	no minimization baseline	pii_handling_and_minimization.md

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Fix in 60 seconds

1. Declare scope
   Enumerate datasets, prompts, models, eval flows. Each must map to a baseline policy.

2. Add ownership
   For every item, tag owner, expiry, and waiver_ref if applicable.

3. Enforce citation-first
   Require cite-then-explain across all governed answers.
   Verify with ΔS and λ probes: stable ≤ 0.45 ΔS, λ convergent.

4. Attach audit hooks
   Every policy enforcement event logs to immutable audit trail.

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Minimal copy-paste checklist

• Datasets rights and licenses verified
• Prompt change control in place
• Model releases tied to governance cards
• Eval gates with sign-off documented
• PII minimization baseline applied
• Risk register updated with waivers

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🔗 Quick-Start Downloads (60 sec)

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

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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