🧭 Quick Return to Map
You are in a sub-page of RAG.
To reorient, go back here:
- RAG — retrieval-augmented generation and knowledge grounding
- 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.
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.
- Visual map and recovery: RAG Architecture & Recovery
- End to end retrieval knobs: Retrieval Playbook
- Traceability schema: Retrieval Traceability
- Snippet contracts: Data Contracts
- Query path splits: Pattern: Query Parsing Split
- Ranking drift: Rerankers
- 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
| Symptom | Likely cause | Open this |
|---|---|---|
| Hybrid returns unrelated snippet | query parsing split not locked | Pattern: Query Parsing Split |
| Hybrid recall < single recall | wrong weighting or missing normalization | Retrieval Playbook |
| Dense retriever dominates BM25 | metric mismatch | Embedding ≠ Semantic |
| Reranker undoes good hits | λ flips, entropy collapse | Rerankers, Entropy Collapse |
-
Measure baseline
Run BM25 alone and dense alone. Log coverage and ΔS. If hybrid < baseline, do not ship. -
Stabilize query parsing
Split HyDE prompts, keyword queries, and dense embeddings into deterministic branches. Lock weighting ratios. -
Reranker probe
Compare recall before and after reranker. If entropy rises, clamp with variance control or drop reranker. -
Enforce snippet schema
Always requiresnippet_id,section_id,offsets,tokens. Hybrid paths must normalize schema fields.
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.| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ” |
| 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 |
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