Eval RAG Precision & Recall — Guardrails and Fix Patterns

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.

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

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

Why precision/recall break in RAG

Goldset drift Anchors no longer align with the corpus after updates. → Fix: refresh goldsets with goldset_curation.md.
Retrieval contract missing Snippet payloads do not include section IDs or offsets. → Fix: enforce Data Contracts.
Precision false positives Semantically near matches but wrong factual anchor. → Fix: rerank with Rerankers.
Recall false negatives Correct snippet exists but chunking or index prevents surfacing. → Fix: re-chunk corpus with chunking-checklist.md.
Evaluation noise Different seeds or paraphrases give unstable results. → Fix: clamp λ variance with variance_and_drift.md.

Quick workflow

Load goldset Each gold QA item must cite snippet_id, section_id, source_url.
Run retrieval Collect top-k results (k = 5, 10, 20).
Log ΔS & λ For each query and paraphrase, record ΔS values and λ states.
Compute metrics
- Precision = correct citations / total citations
- Recall = correct citations / gold references
Regression gate Block deploy if precision < 0.75 or recall < 0.70.

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": "→"
}

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.

🔗 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

🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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