# Evaluation & Guardrails — Global Fix Map Prove fixes work and won’t regress. Detect “double hallucination,” enforce acceptance gates, and keep pipelines auditable. ## What this page is - A compact playbook to evaluate RAG quality and reasoning stability - Drop-in guardrails that catch failure before users see it - CI-ready acceptance targets you can copy ## When to use - You “fixed it” but cannot show measurable improvement - Answers look plausible yet citations or snippets don’t line up - Performance flips between seeds, sessions, or agent mixes - Latency tuning changes accuracy in non-obvious ways ## Open these first - RAG precision/recall spec: [RAG Precision & Recall](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_rag_precision_recall.md) - Latency versus accuracy method: [Latency vs Accuracy](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_latency_vs_accuracy.md) - Cross-agent agreement tests: [Cross-Agent Consistency](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_cross_agent_consistency.md) - Semantic stability checks: [Semantic Stability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_semantic_stability.md) - Why-this-snippet schema: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) - Snippet & citation schema: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) --- ## Common evaluation pitfalls - **Double hallucination** metrics focus on style or BLEU but ignore snippet fidelity - **Recall illusion** top-k looks high while ΔS(question, context) stays risky - **Seed lottery** single-seed wins mask instability across paraphrases - **Hybrid flapping** HyDE+BM25 mixes shift rank order between runs - **Guardrail over-clamp** rigid filters “fix” tone but not logic boundaries - **Benchmark mismatch** eval set does not reflect OCR noise or multilingual drift - **No trace table** cannot audit which snippet justified the answer --- ## Fix in 60 seconds 1) **Adopt acceptance gates** - Retrieval sanity: token overlap ≥ 0.70 to the target section - ΔS(question, context) ≤ 0.45 on the median of the suite - λ_observe stays convergent on 3 paraphrases 2) **Require citations before prose** - Enforce cite-then-answer with [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) - Store a trace table: question, retrieved ids, snippet spans, ΔS, λ 3) **Stability before speed** - Plot latency vs accuracy and pin the knee point See [Latency vs Accuracy](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_latency_vs_accuracy.md) 4) **Cross-agent cross-check** - Compare two capable models on the same context See [Cross-Agent Consistency](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_cross_agent_consistency.md) 5) **Regression fence in CI** - Fail the build if ΔS median rises above 0.45 or trace coverage drops below 0.70 See [RAG Precision & Recall](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_rag_precision_recall.md) --- ## Copy paste prompt ``` You have TXT OS and the WFGY Problem Map. Goal Add measurable guardrails to my RAG pipeline and prove the fix. Tasks 1. Build a 20-item smoke suite with: * question, expected section anchor, and gold snippet span * bilingual paraphrases for 5 items (if multilingual) 2. Run WFGY probes: * compute ΔS(question, context) for each item * record λ\_observe at retrieval and reasoning * require cite-then-answer and log a trace table 3. Report acceptance: * token overlap to anchor (coverage) * ΔS median and interquartile range * paraphrase stability (λ stays convergent) * pass/fail against thresholds 4. Plot latency vs accuracy and select a stable operating point. Output * The trace table (csv/markdown) * Acceptance summary and which items failed * A one-page decision note on whether to ship ``` --- ## Minimal checklist - Trace table saved with citations and snippet spans - ΔS computed per item; λ recorded at retrieval and reasoning - Coverage ≥ 0.70 to the referenced section for direct QA - Cross-agent consistency measured on a subset - Latency vs accuracy chart archived with the run id ## Acceptance targets - ΔS(question, context) median ≤ **0.45** on the suite - λ **convergent** across 3 paraphrases per item - **≥ 0.70** token overlap to the gold section for direct QA items - No unexplained rank flips when toggling hybrid retrieval - CI blocks merges when any target fails --- ### 🔗 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. > GitHub stars ⭐ [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).
[![WFGY Main](https://img.shields.io/badge/WFGY-Main-red?style=flat-square)](https://github.com/onestardao/WFGY)   [![TXT OS](https://img.shields.io/badge/TXT%20OS-Reasoning%20OS-orange?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS)   [![Blah](https://img.shields.io/badge/Blah-Semantic%20Embed-yellow?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)   [![Blot](https://img.shields.io/badge/Blot-Persona%20Core-green?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)   [![Bloc](https://img.shields.io/badge/Bloc-Reasoning%20Compiler-blue?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)   [![Blur](https://img.shields.io/badge/Blur-Text2Image%20Engine-navy?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)   [![Blow](https://img.shields.io/badge/Blow-Game%20Logic-purple?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)