# Eval Observability — Coverage Tracking
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> **Evaluation disclaimer (coverage tracking)**
> Coverage numbers here measure how much of a designed space you have touched under chosen tests.
> High coverage does not guarantee absence of bugs or failures outside those tests.
---
A focused module to monitor **retrieval coverage** during eval and live runs.
Coverage answers the key question: *“Did we retrieve enough of the right section to support the answer?”*
---
## Why coverage tracking matters
- **False negatives**: The right fact exists, but snippets cover too little of the section.
- **Over-fragmentation**: Documents chunked too aggressively result in coverage <0.50 despite correct snippets.
- **Hallucinations**: When coverage is low, LLMs often fill gaps with fabrications.
- **Eval blind spots**: Benchmarks without coverage probes miss systematic recall failures.
---
## Core definition
Coverage is defined as:
```text
coverage = retrieved_tokens_in_target_section / total_tokens_in_target_section
````
* **Target section** = gold label or expected answer span.
* **Threshold** = minimum 0.70 in most RAG tasks.
* **Tolerance** = allow 5–10% batch queries below threshold before raising alert.
---
## Probe design
1. **Annotate gold sets**
For each eval question, mark the expected source section IDs and token spans.
2. **Measure per-query coverage**
Count how many tokens from expected span were retrieved.
Normalize by total tokens in span.
3. **Batch aggregation**
Track percentage of queries below threshold.
Report average coverage ± variance.
4. **Drift detection**
Compare against historical baseline (previous model or retriever version).
If drop >0.05, escalate to retriever/infrastructure team.
---
## Alert thresholds
| Metric | Warning | Critical |
| ------------------ | ---------- | ---------- |
| Per-query coverage | <0.70 | <0.60 |
| Batch pass rate | <0.90 | <0.80 |
| Drift vs baseline | drop >0.05 | drop >0.10 |
---
## Example probe code (pseudo)
```python
def track_coverage(retrieved, target_span):
overlap = count_tokens(retrieved, target_span)
coverage = overlap / len(target_span)
return coverage
for q in eval_batch:
cov = track_coverage(q.retrieved_tokens, q.gold_span)
if cov < 0.70:
alerts.append({"qid": q.id, "coverage": cov})
```
---
## Common pitfalls
* **Ignoring multi-section answers** → coverage must sum across all required sections.
* **Only measuring top-1 snippet** → always include top-k, otherwise underestimation occurs.
* **Static thresholds** → thresholds should adapt to doc size and retrieval depth.
* **No historical baseline** → without drift tracking, regressions pass unnoticed.
---
## Reporting dashboards
* **Histograms** of per-query coverage distribution.
* **Trend lines** for batch averages across eval sets.
* **Drift deltas** vs baseline runs.
* **Heatmaps** showing coverage by document or domain.
---
### 🔗 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
| 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 tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
| ⚙️ 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 taxonomy and fix map |
| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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[](https://github.com/onestardao/WFGY)