Audit and Logging — Guardrails and Fix Pattern

This page defines auditability standards for AI pipelines.
Without consistent logging, you cannot prove compliance, detect drift, or reproduce failures.
Use this guide to lock observability into ingestion, retrieval, reasoning, and generation steps.

When to use this page

You need verifiable traces for legal, regulatory, or enterprise compliance.
Investigations require replay of a user query and its retrieval sources.
You must detect hallucinations or drift in production runs.
Customers or auditors ask for explainability and reproducibility.

Acceptance targets

Logs capture ΔS and λ states at every RAG/reasoning step.
≥ 95% of user queries have matching citation and snippet logs.
Audit trail includes source corpus, license_id, and index version.
Drift alerts trigger when ΔS ≥ 0.60 or λ flips divergent across seeds.
Replay is possible within 5 minutes for any production query.

Common failures → exact fixes

Symptom	Likely cause	Open this
Retrieval answers not reproducible	no snippet_id trace	retrieval-traceability.md
Citations missing or out of sync	no schema contract in logs	data-contracts.md
No evidence of dataset license in audit	ingestion lacks rights metadata	license_and_dataset_rights.md
ΔS or λ not recorded	metrics missing in pipeline	deltaS_thresholds.md, lambda_observe.md
Drift appears only in production, not tests	no live probes	live_monitoring_rag.md

Fix in 60 seconds

Traceability schema
Require snippet_id, section_id, source_url, offsets, tokens in every retrieval log.
Metrics capture
Record ΔS and λ per retrieval and reasoning step.
Rights + versioning
Always log license_id, rights_holder, and index_hash.
Live probes
Stream ΔS ≥ 0.60 alerts to monitoring dashboards.
Replayable store
Store logs in immutable KV or append-only DB. Replay query with same index_hash.

Minimal audit checklist

Logs stored in append-only or write-once medium.
Each retrieval step includes ΔS, λ, snippet schema.
Each generation step includes citations and source anchors.
Expired datasets flagged in logs.
Replay command tested weekly.

🔗 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 →

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