Eval Observability — Evaluation Playbook

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Evaluation disclaimer (observability playbook)
The signals in this playbook come from specific logging and probing setups.
They are tools for monitoring behavior, not proofs of safety or correctness on their own.

A compact playbook to stabilize evaluation and ensure results are reproducible.
Use this when metrics look inconsistent, coverage drifts, or benchmarks feel untrustworthy.

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 to target section
λ remains convergent across 3 paraphrases and 2 seeds
Variance ratio ≤ 0.15 across seed runs
No monotonic downward drift beyond 3 windows

Step 1 — Regression Gate

Run a gate before promoting new models or retrievers.

Require ΔS ≤ 0.45 and coverage ≥ 0.70 on 3 paraphrases.
Block deploy if λ flips divergent.
Open: regression_gate.md

Step 2 — Alerting and Probes

Set live probes to catch collapse early.

Alert when ΔS ≥ 0.60 or λ flips.
Emit logs for coverage gaps, drift slopes, entropy rises.
Open: alerting_and_probes.md

Step 3 — Coverage Tracking

Measure retrieved vs target section alignment.

At least 70% coverage on gold targets.
Track section-level recall, not just overall hit-rate.
Open: coverage_tracking.md

Step 4 — ΔS Thresholds

Normalize ΔS scoring and enforce thresholds.

Stable < 0.40
Transitional 0.40–0.60
Collapse ≥ 0.60
Open: deltaS_thresholds.md

Step 5 — λ Observe

Use λ probes to detect schema drift.

Run same query across seeds and paraphrases.
If λ flips, lock schema and enforce reranker.
Open: lambda_observe.md

Step 6 — Variance and Drift

Log variance ratios and detect slow regressions.

Variance ratio ≤ 0.15
Drift slope ≤ 0.02 per batch
Alert if monotonic decline lasts 3+ windows
Open: variance_and_drift.md

Step 7 — Audit & Escalation

If instability remains:

Rebuild embeddings with embedding-vs-semantic.md
Patch retriever fragmentation with vectorstore-fragmentation.md
Apply reranking schema: rerankers.md

Copy-paste eval contract

eval_contract:
  seeds: 3
  paraphrases: 3
  targets:
    deltaS: <=0.45
    coverage: >=0.70
    lambda: convergent
    variance: <=0.15
    drift: <=0.02
alerts:
  - deltaS >=0.60
  - lambda divergent
  - drift slope >0.02

🔗 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

Module	Description	Link
WFGY Core	Canonical framework entry point	View
Problem Map	Diagnostic map and navigation hub	View
Tension Universe Experiments	MVP experiment field	View
Recognition	Where WFGY is referenced or adopted	View
AI Guide	Anti-hallucination reading protocol for tools	View

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