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147 lines
6.3 KiB
Markdown
147 lines
6.3 KiB
Markdown
# Eval Observability — Coverage Tracking
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **Eval_Observability**.
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> To reorient, go back here:
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>
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> - [**Eval_Observability** — evaluation metrics and system observability](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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> **Evaluation disclaimer (coverage tracking)**
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> Coverage numbers here measure how much of a designed space you have touched under chosen tests.
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> High coverage does not guarantee absence of bugs or failures outside those tests.
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---
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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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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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