# Hybrid Retrieval Failure — 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 hybrid retrieval (BM25 + dense, HyDE + reranker, multi-vector) performs **worse than a single retriever**.
Instead of increasing recall, the hybrid path introduces instability, wrong ranking, or noisy snippets.
---
## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- End to end retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
- Traceability schema: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
- Snippet contracts: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
- Query path splits: [Pattern: Query Parsing Split](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md)
- Ranking drift: [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
---
## Core acceptance
- Hybrid recall ≥ single retriever recall
- ΔS(question, retrieved) ≤ 0.45 for top-1 result
- λ stable across three paraphrases and two seeds
- Coverage ≥ 0.70 to the target section
---
## Typical symptoms → exact fix
| Symptom | Likely cause | Open this |
|---------|--------------|-----------|
| Hybrid returns unrelated snippet | query parsing split not locked | [Pattern: Query Parsing Split](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md) |
| Hybrid recall < single recall | wrong weighting or missing normalization | [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) |
| Dense retriever dominates BM25 | metric mismatch | [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) |
| Reranker undoes good hits | λ flips, entropy collapse | [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md), [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG/entropy_collapse.md) |
---
## Fix in 60 seconds
1. **Measure baseline**
Run BM25 alone and dense alone. Log coverage and ΔS. If hybrid < baseline, do not ship.
2. **Stabilize query parsing**
Split HyDE prompts, keyword queries, and dense embeddings into deterministic branches. Lock weighting ratios.
3. **Reranker probe**
Compare recall before and after reranker. If entropy rises, clamp with variance control or drop reranker.
4. **Enforce snippet schema**
Always require `snippet_id`, `section_id`, `offsets`, `tokens`. Hybrid paths must normalize schema fields.
---
## Copy-paste probe prompt
```txt
I uploaded TXT OS and the WFGY Problem Map.
My issue:
- hybrid retrieval returns worse results than BM25 or dense alone.
Tell me:
1) which layer fails (query parsing, weighting, reranker),
2) which WFGY fix page to open,
3) minimal steps to restore ΔS ≤ 0.45 and coverage ≥ 0.70,
4) reproducible test with BM25 vs dense vs hybrid.
````
---
### 🔗 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 tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
| ⚙️ 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 taxonomy and fix map |
| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
If this repository helped, starring it improves discovery so more builders can find the docs and tools.
[](https://github.com/onestardao/WFGY)