# Index Skew — Guardrails and Fix Pattern
🧭 Quick Return to Map
> You are in a sub-page of **RAG**.
> To reorient, go back here:
>
> - [**RAG** — retrieval-augmented generation and knowledge grounding](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
>
> 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.
When the index reports "healthy" (no errors, embeddings ingested, stats normal) but **retrieval still fails**:
coverage is low, ΔS unstable, or retrieved snippets are inconsistent with ground truth.
This indicates an **index skew** between data reality and retrieval semantics.
---
## Open these first
- Visual recovery map: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- Retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
- Embedding misalignment: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
- Chunk sizing: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
- Store-level fragmentation: [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/vectorstore-fragmentation.md)
- Snippet contracts: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
---
## Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 for target section
- λ stable across three paraphrases and two seeds
- E_resonance flat across long windows
---
## Typical symptoms → exact fix
| Symptom | Likely cause | Open this |
|---------|--------------|-----------|
| Index "ready" but recall < 0.50 | embedding misaligned vs semantic intent | [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) |
| Repeated snippets, poor coverage | store fragmentation or duplicate collapse | [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/vectorstore-fragmentation.md) |
| Right section exists but not hit | chunk too large/small or mis-boundary | [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) |
| Citations drift across runs | contract not enforced | [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) |
---
## Fix in 60 seconds
1. **Probe recall**
Run a gold QA set against index. If coverage < 0.70, suspect skew.
2. **Re-embed with semantic normalization**
Normalize casing, accents, whitespace. Enforce same tokenizer across queries and index.
3. **Chunk audit**
Verify chunk boundaries. Adjust stride/overlap until ΔS converges.
4. **Fragmentation sweep**
Drop near-duplicate vectors. Rebuild FAISS/HNSW indexes with fresh IDs.
5. **Contract enforcement**
Require `snippet_id`, `section_id`, `offsets`, `tokens` for every retrieval.
---
## Copy-paste probe prompt
```txt
I uploaded TXT OS and the WFGY Problem Map.
My RAG issue:
- Index shows healthy but retrieval recall is low.
- ΔS across probes = 0.62, coverage = 0.45.
Tell me:
1) is it embedding misalignment, chunking skew, or vectorstore fragmentation,
2) which WFGY fix page to open,
3) minimal steps to restore ΔS ≤ 0.45 and coverage ≥ 0.70,
4) reproducible test set to confirm.
````
---
### 🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ” |
| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/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](/recognition/README.md) | External citations, integrations, and ecosystem proof |
| Engine | [WFGY 1.0](/legacy/README.md) | Original PDF based tension engine |
| Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents |
| Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine, 131 S class set |
| Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure checklist and fix map |
| Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline |
| Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer |
| Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix |
| Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 |
| Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers |
| App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot |
| App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS |
| App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control |
| App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo |
If this repository helped, starring it improves discovery so more builders can find the docs and tools.
[](https://github.com/onestardao/WFGY)