WFGY/ProblemMap/GlobalFixMap/RAG/hybrid_failure.md

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
• 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.

---

Open these first

• 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

---

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
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

---

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

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	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

---

Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

If this repository helped, starring it improves discovery so more builders can find the docs and tools.