Policy Baseline — Guardrails and Fix Pattern

🧭 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.

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.”

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.

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

Fix in 60 seconds

Declare scope
Enumerate datasets, prompts, models, eval flows. Each must map to a baseline policy.
Add ownership
For every item, tag owner, expiry, and waiver_ref if applicable.
Enforce citation-first
Require cite-then-explain across all governed answers.
Verify with ΔS and λ probes: stable ≤ 0.45 ΔS, λ convergent.
Attach audit hooks
Every policy enforcement event logs to immutable audit trail.

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

🔗 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

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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