Eval RAG Precision & Recall

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Evaluation disclaimer (RAG precision and recall)
Precision and recall here are computed in a controlled RAG scenario with specific data and judgement rules.
They should be used to debug retrieval behavior, not as general claims about model intelligence.

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This page defines how to measure precision and recall in RAG pipelines under the WFGY framework. It sets acceptance thresholds, common pitfalls, and structural fixes to keep evaluations meaningful and reproducible.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• Retrieval contract: Data Contracts
• Traceability schema: Retrieval Traceability
• Embedding drift: Embedding ≠ Semantic
• Hallucination boundaries: Hallucination

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Acceptance targets

• Precision ≥ 0.75 at citation level
• Recall ≥ 0.70 of gold anchor snippets
• ΔS(question, retrieved) ≤ 0.45 for majority of pairs
• λ remains convergent across 3 paraphrases and 2 random seeds
• Evaluations must be auditable & reproducible with JSON logs

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Why precision/recall break in RAG

1. Goldset drift Anchors no longer align with the corpus after updates. → Fix: refresh goldsets with goldset_curation.md.

2. Retrieval contract missing Snippet payloads do not include section IDs or offsets. → Fix: enforce Data Contracts.

3. Precision false positives Semantically near matches but wrong factual anchor. → Fix: rerank with Rerankers.

4. Recall false negatives Correct snippet exists but chunking or index prevents surfacing. → Fix: re-chunk corpus with chunking-checklist.md.

5. Evaluation noise Different seeds or paraphrases give unstable results. → Fix: clamp λ variance with variance_and_drift.md.

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Quick workflow

1. Load goldset Each gold QA item must cite snippet_id, section_id, source_url.

2. Run retrieval Collect top-k results (k = 5, 10, 20).

3. Log ΔS & λ For each query and paraphrase, record ΔS values and λ states.

4. Compute metrics

   • Precision = correct citations / total citations
   • Recall = correct citations / gold references

5. Regression gate Block deploy if precision < 0.75 or recall < 0.70.

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Example JSON log

{
  "question": "What causes hallucination re-entry?",
  "gold": ["hallucination-reentry"],
  "retrieved": ["hallucination-reentry", "entropy-drift"],
  "precision": 0.50,
  "recall": 1.00,
  "ΔS": 0.38,
  "λ_state": "→"
}

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Common pitfalls

• Evaluating only precision → recall collapses unnoticed.
• Counting fuzzy hits as correct → ΔS may be high, but factually wrong.
• No paraphrases tested → λ instability hidden.
• Relying on one seed → fragile numbers that don’t generalize.

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🔗 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 + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

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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