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ProblemMap/GlobalFixMap/Eval_Observability/coverage_tracking.md
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ProblemMap/GlobalFixMap/Eval_Observability/coverage_tracking.md
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# Eval Observability — Coverage Tracking
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A focused module to monitor **retrieval coverage** during eval and live runs.
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Coverage answers the key question: *“Did we retrieve enough of the right section to support the answer?”*
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---
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## Why coverage tracking matters
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- **False negatives**: The right fact exists, but snippets cover too little of the section.
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- **Over-fragmentation**: Documents chunked too aggressively result in coverage <0.50 despite correct snippets.
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- **Hallucinations**: When coverage is low, LLMs often fill gaps with fabrications.
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- **Eval blind spots**: Benchmarks without coverage probes miss systematic recall failures.
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---
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## Core definition
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Coverage is defined as:
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```text
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coverage = retrieved_tokens_in_target_section / total_tokens_in_target_section
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````
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* **Target section** = gold label or expected answer span.
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* **Threshold** = minimum 0.70 in most RAG tasks.
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* **Tolerance** = allow 5–10% batch queries below threshold before raising alert.
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---
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## Probe design
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1. **Annotate gold sets**
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For each eval question, mark the expected source section IDs and token spans.
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2. **Measure per-query coverage**
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Count how many tokens from expected span were retrieved.
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Normalize by total tokens in span.
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3. **Batch aggregation**
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Track percentage of queries below threshold.
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Report average coverage ± variance.
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4. **Drift detection**
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Compare against historical baseline (previous model or retriever version).
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If drop >0.05, escalate to retriever/infrastructure team.
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---
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## Alert thresholds
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| Metric | Warning | Critical |
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| ------------------ | ---------- | ---------- |
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| Per-query coverage | <0.70 | <0.60 |
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| Batch pass rate | <0.90 | <0.80 |
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| Drift vs baseline | drop >0.05 | drop >0.10 |
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---
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## Example probe code (pseudo)
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```python
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def track_coverage(retrieved, target_span):
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overlap = count_tokens(retrieved, target_span)
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coverage = overlap / len(target_span)
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return coverage
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for q in eval_batch:
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cov = track_coverage(q.retrieved_tokens, q.gold_span)
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if cov < 0.70:
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alerts.append({"qid": q.id, "coverage": cov})
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```
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---
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## Common pitfalls
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* **Ignoring multi-section answers** → coverage must sum across all required sections.
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* **Only measuring top-1 snippet** → always include top-k, otherwise underestimation occurs.
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* **Static thresholds** → thresholds should adapt to doc size and retrieval depth.
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* **No historical baseline** → without drift tracking, regressions pass unnoticed.
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---
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## Reporting dashboards
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* **Histograms** of per-query coverage distribution.
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* **Trend lines** for batch averages across eval sets.
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* **Drift deltas** vs baseline runs.
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* **Heatmaps** showing coverage by document or domain.
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **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>” |
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| **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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---
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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| 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) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| 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) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| 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) |
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| 🧙♂️ 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) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="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).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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