Phase 1: Evaluation foundations
Define the task, allowed outcomes, severe failures, representative slices, ground truth, rubrics, thresholds, and ownership before comparing systems. Learn uncertainty, sampling, inter-rater agreement, and the limits of automated judges.
Phase 2: Component and end-to-end measures
Measure retrieval candidates, authorization, freshness, answer correctness, claim support, citations, abstention, safety, tool trajectories, latency, reliability, and cost separately and together.
Phase 3: AI observability
Instrument gateways, retrieval, reranking, model calls, policy checks, tools, approvals, and validators with correlated traces and safe metrics. Minimize content, bound cardinality, and protect telemetry access and retention.
Phase 4: Release and incident operations
Create canary gates, quality SLOs, alerts, runbooks, rollback, and post-incident regression workflows. Demonstrate that a severe semantic or security failure can be detected, contained, explained, and prevented from recurring.
Twelve-week plan
| Weeks | Practice | Deliverable |
|---|---|---|
| 1-3 | Datasets, labels, rubrics, uncertainty | Versioned evaluation contract |
| 4-6 | Retrieval, answer, citation, safety metrics | Component scorecard |
| 7-9 | OpenTelemetry, SLOs, dashboards, alerts | AI control-room dashboard |
| 10-12 | Canary, rollback, incident exercise | Release and incident evidence |
Practical checklist
- Evaluation set versioned
- Severe failures gated separately
- Automated judges calibrated
- Trace content minimized
- Quality and operational SLOs owned
- Rollback and recovery tested
First-party sources
Source status last checked 2026-09-11. Links can change after publication.