Policy Baseline — Guardrails and Fix Pattern

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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →