Eval Observability — Alerting and Probes

🧭 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.

A live guardrail system that detects semantic drift, retrieval collapse, and logic instability in production.
Use this page to design continuous probes (ΔS, λ, coverage, resonance) and trigger alerts before users see failures.

Why probes are required

Silent regressions: Models may degrade gradually after retraining or infra changes.
Runtime entropy: Long chains often destabilize after 25–40 steps.
Hybrid stack drift: Store upgrades, reranker weights, or tokenizer shifts silently change outcomes.
Auditability: Real-time probes make every failure reproducible.

Core probe dimensions

Metric	Probe type	Alert condition
ΔS(question, retrieved)	per-query	≥ 0.60 for any query OR average >0.50 across batch
Coverage of target section	per-query	< 0.70 for more than 5% of batch
λ_observe	rolling window	divergence observed across 2 seeds or paraphrases
E_resonance	sliding horizon	spikes or oscillations in 50–100 step runs

Recommended probe architecture

Pre-query probe
Check retriever config hash, index hash, analyzer type.
Block if mismatched against gold baseline.
Mid-query probe
During retrieval, compute ΔS(question, retrieved).
Attach snippet IDs and offsets for traceability.
Post-query probe
Run λ stability check across 3 paraphrases.
If divergent, tag output with unstable=true.
Long-chain probe
For conversations >25 steps, sample entropy and E_resonance every 10 steps.
Trigger backoff or memory split if instability rises.

Alerting routes

Slack / Teams → Send structured JSON logs with ΔS, λ, coverage, index hash, retriever config.
PagerDuty → Trigger incidents only when threshold failures exceed N in M minutes.
Dashboards → Grafana/Datadog with ΔS trend lines, λ variance plots, coverage histograms.
Audit store → Write all probe outputs to KV or DB keyed by (session_id, index_hash, retriever_config).

Example probe config (YAML)

probes:
  deltaS:
    threshold: 0.60
    action: alert
  coverage:
    threshold: 0.70
    tolerance: 0.05
  lambda:
    seeds: 2
    paraphrases: 3
    action: block
  resonance:
    horizon: 100
    action: degrade_notice
alerting:
  sink:
    - slack
    - pagerduty
    - grafana

Common mistakes

Only probing ΔS. Always include coverage + λ + resonance.
Static thresholds. Thresholds must be tested against gold sets and updated quarterly.
No audit linkage. Alerts without index hash and retriever config cannot be replayed.
Flooding alerts. Use capped retries and aggregate rules to avoid pager fatigue.

🔗 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.

6.2 KiB Raw Blame History Unescape Escape