Section 183 · Companion Reference, Testing AI
Appendix: AI Quality Release Checklist
A good release checklist turns uncertainty into a decision instead of a meeting full of vibes.
monitoringlatencyRAGrollbackquality release checklist
What to do
- Start with the evaluation target.
- Check the rubric and judge.
- Check operational quality.
- Review p50, p95, and p99 latency, token usage, cost per successful outcome, retry loops, cache behavior, and tool-call count.
- Check privacy, security, and compliance.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for monitoring, latency, RAG, rollback needed to reproduce work on Appendix: AI Quality Release Checklist.
- Report results for monitoring, latency, RAG, rollback by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Checklists should be versioned and postmortem-driven. Every incident should update the release checklist so the organization learns structurally.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "Appendix: AI Quality Release Checklist." Testing AI Knowledge Edition, section 183.
https://jarbon.ai/testing-ai/knowledge/ch183-quality-release-checklist.html