Turn comparison evidence into a clear ship or hold decision.
Bring baseline, candidate, critical regressions, prompt and model versions, reviewer notes, cost deltas, and acceptance policy into one release record.
gpt-5.6 · prompt v19
Policy refusals improved, but fallback routing raises successful-task cost above the approved ceiling.
Leave this stage with evidence the next one can use.
Each stage is configured around the customer's application, risk, quality standard, operational ownership, and release policy.
Explicit release policy
Define required metrics, critical failure modes, segment thresholds, and minimum coverage.
Versioned decision record
Preserve the prompt, model, configuration, dataset, evaluators, and evidence used to decide.
Human review where needed
Route material regressions, policy exceptions, and uncertain cases to named reviewers.
Controlled promotion
Record approve, hold, exception, rollback, and rollout status without losing the rationale.
No stage operates in isolation.
The evidence links traces, versions, evaluation runs, release gates, production monitors, and cost attribution.
Practical thinking for production AI quality.
What belongs in an AI release review
A concise evidence packet for quality, behavior, cost, latency, security, ownership, and rollback.
Prompt version governance without slowing engineering
Control prompt changes with readable diffs, evaluation evidence, environment labels, ownership, and rollback history.
A readiness review for model migrations
Compare quality, behavior, latency, context limits, tool use, safety, and cost before switching providers or model families.
Define what must be true before the candidate ships.
Bring critical regressions, reviewer judgment, acceptance policy, and promotion state into one accountable release record.