Eval Observability — Coverage Tracking

🧭 Quick Return to Map

You are in a sub-page of Eval_Observability.
To reorient, go back here:

Eval_Observability — evaluation metrics and system observability

WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

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:

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

Annotate gold sets For each eval question, mark the expected source section IDs and token spans.
Measure per-query coverage Count how many tokens from expected span were retrieved. Normalize by total tokens in span.
Batch aggregation Track percentage of queries below threshold. Report average coverage ± variance.
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)

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	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text 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	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

If this repository helped, starring it improves discovery so more builders can find the docs and tools.

6.4 KiB Raw Blame History Unescape Escape