# Eval RAG Precision & Recall — Guardrails and Fix Patterns

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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.

---

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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
* Retrieval contract: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
* Traceability schema: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
* Embedding drift: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
* Hallucination boundaries: [Hallucination](https://github.com/onestardao/WFGY/blob/main/ProblemMap/hallucination.md)

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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](./goldset_curation.md).

2. **Retrieval contract missing**
   Snippet payloads do not include section IDs or offsets.
   → Fix: enforce [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md).

3. **Precision false positives**
   Semantically near matches but wrong factual anchor.
   → Fix: rerank with [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md).

4. **Recall false negatives**
   Correct snippet exists but chunking or index prevents surfacing.
   → Fix: re-chunk corpus with [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md).

5. **Evaluation noise**
   Different seeds or paraphrases give unstable results.
   → Fix: clamp λ variance with [variance\_and\_drift.md](../Eval_Observability/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.

---

## Example JSON log

```json
{
  "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.

---

### 🔗 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 + \<your question>”   |
| **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 |

---

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### 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 based tension engine |
| Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents |
| 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 checklist and fix map |
| Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline |
| Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer |
| Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix |
| Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 |
| Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers |
| App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot |
| App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS |
| App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control |
| App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo |

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