# Evaluation & Guardrails — Global Fix Map
🏥 Quick Return to Emergency Room
> You are in a specialist desk.
> For full triage and doctors on duty, return here:
>
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md)
>
> Think of this page as a sub-room.
> If you want full consultation and prescriptions, go back to the Emergency Room lobby.
A hub to **prove fixes actually work and won’t regress**.
Use this folder when you want to validate that your RAG / LLM pipeline changes are stable, measurable, and reproducible.
The goal is to prevent “double hallucination,” enforce acceptance gates, and keep evaluation pipelines auditable.
---
## What this page is
- A compact playbook to evaluate RAG quality and reasoning stability
- Drop-in guardrails that catch failures before users see them
- CI/CD-ready acceptance targets you can copy directly
---
## When to use
- You shipped a fix but cannot show measurable improvement
- Answers look plausible but citations or snippets don’t match
- Performance flips between seeds, sessions, or agent mixes
- Latency tuning silently changes accuracy
- Your team disagrees on whether a fix is “actually better”
---
## Open these first
- RAG precision/recall spec → [eval_rag_precision_recall.md](./eval_rag_precision_recall.md)
- Latency versus accuracy method → [eval_latency_vs_accuracy.md](./eval_latency_vs_accuracy.md)
- Cross-agent agreement tests → [eval_cross_agent_consistency.md](./eval_cross_agent_consistency.md)
- Semantic stability checks → [eval_semantic_stability.md](./eval_semantic_stability.md)
- Why-this-snippet schema → [retrieval-traceability.md](../retrieval-traceability.md)
- Snippet & citation schema → [data-contracts.md](../data-contracts.md)
---
## Common evaluation pitfalls
- **Double hallucination** → Metrics look good (BLEU, ROUGE) but answers cite the wrong snippet
- **Recall illusion** → Top-k recall seems fine, yet ΔS(question, context) is still unstable
- **Seed lottery** → Success on one random seed hides instability across paraphrases
- **Hybrid flapping** → HyDE + BM25 mixes reorder results differently every run
- **Over-clamping** → Filters enforce tone but fail to fix logical drift
- **Benchmark mismatch** → Eval set ignores OCR noise or multilingual inputs
- **No trace table** → You cannot audit which snippet was cited
---
## Fix in 60 seconds
1. **Adopt acceptance gates**
- Retrieval sanity: token overlap ≥ 0.70 to the gold section
- ΔS(question, context) ≤ 0.45 on median across suite
- λ_observe stays convergent across 3 paraphrases
2. **Require citations first**
- Enforce cite-then-answer with [data-contracts.md](../data-contracts.md)
- Log: question, retrieved ids, snippet spans, ΔS, λ
3. **Stability before speed**
- Always measure latency vs accuracy before tuning
- See [eval_latency_vs_accuracy.md](./eval_latency_vs_accuracy.md)
4. **Cross-agent cross-check**
- Run 2 strong models on the same retrieval
- See [eval_cross_agent_consistency.md](./eval_cross_agent_consistency.md)
5. **Regression fence in CI**
- Block merges if ΔS median > 0.45 or coverage < 0.70
- See [eval_rag_precision_recall.md](./eval_rag_precision_recall.md)
---
## Minimal checklist
- Trace table saved (citations + snippet spans)
- ΔS computed per item; λ recorded at retrieval & reasoning
- Coverage ≥ 0.70 to gold snippet
- Cross-agent agreement tested
- Latency vs accuracy chart archived with run id
---
## Acceptance targets
- ΔS(question, context) median ≤ **0.45**
- λ **convergent** across 3 paraphrases
- Token overlap ≥ **0.70** to gold snippet
- No unexplained rank flips on hybrid retrievers
- CI blocks merges when targets fail
---
## FAQ
**Q: What is ΔS and why does it matter?**
A: ΔS measures semantic distance between your query and retrieved context. Values above 0.45 indicate unstable retrieval, even if the snippet looks similar.
**Q: Why not just trust BLEU/ROUGE?**
A: They score surface similarity, not factual correctness. A fluent but wrong answer can pass BLEU. WFGY gates enforce snippet fidelity.
**Q: What does λ_observe mean?**
A: λ_observe tracks whether paraphrased queries converge on the same retrieval. Divergence shows instability that will confuse users.
**Q: How do I build a trace table?**
A: For every eval item, log `question`, `retrieved ids`, `snippet spans`, `ΔS`, `λ_state`. This makes your pipeline auditable later.
**Q: Do I need a big eval set?**
A: No. Start with 20 smoke-test items, including multilingual or noisy samples. Scale up only after you pass basic gates.
**Q: What if latency tuning drops accuracy?**
A: Always plot latency vs accuracy. Use the knee point of the curve, not the fastest or slowest configuration.
---
### 🔗 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 + \” |
| **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 |
---
### 🧭 Explore More
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
> Engineers, hackers, and open source builders who supported WFGY from day one.
> 
⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
[](https://github.com/onestardao/WFGY)
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