🧭 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 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.
- 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.
- 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.
| 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 |
-
Traceability schema
Requiresnippet_id, section_id, source_url, offsets, tokensin every retrieval log. -
Metrics capture
Record ΔS and λ per retrieval and reasoning step. -
Rights + versioning
Always loglicense_id,rights_holder, andindex_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.
- 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.
| 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 |
| 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 |
If this repository helped, starring it improves discovery so more builders can find the docs and tools.
