Chapter 20
The Practical Playbook
Turn the book into a concrete operating system for a team or repo. Start with a small quality system: cases, repeated runs, traces, rubric, slices, gate, monitor, and incident loop. Use fail-safe defaults and incident promotion so production failures become future eval cases…
Apply this chapter
- Turn the book into a concrete operating system for a team or repo.
- Start with a small quality system: cases, repeated runs, traces, rubric, slices, gate, monitor, and incident loop.
- Use fail-safe defaults and incident promotion so production failures become future eval cases.
- Make quality visible and interesting enough that people actually maintain it.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
11 focused briefs
Concepts in this chapter
- 164Testing a ChatbotChatbots need more than answer checks. Confidence Engineers must evaluate multi-turn behavior, grounding, safety, tone, memory, escalation, and…
- 165Worked Example: Testing a Customer-Support ChatbotA full AI quality workflow shows how the pieces of the book fit together.
- 166Governance for AI QualityAI quality needs ownership, decision rights, audit trails, and escalation paths before the incident happens.
- 167Failure Taxonomy for AI SystemsA shared failure language helps teams cluster problems instead of drowning in disconnected bug reports.
- 168AI Always FailsThe useful question is not whether AI will fail. It is where, how often, how badly, and whether you already know which inputs are likely to…
- 169Failure Modes and Fail-Safe AIThe safest AI systems are designed so likely failures become bounded, visible, reversible, and boring instead of catastrophic.
- 170Measurement Infrastructure Must Know About VarianceIf the measurement system ignores its own variance, it will eventually promote lucky noise as product improvement.
- 171Performance Engineering for AI SystemsPerformance is no longer just a test at the end. For AI systems, it is an engineering discipline tied directly to quality, cost, reliability…
- 172Minimum Viable AI Quality SystemIf the book feels large, start here: a small quality system that produces real evidence instead of ritual.
- 173Make Testing InterestingQuality work gets better when people actually want to read the failures.
- 190Agentic Frameworks vs. Parameterized WorkflowsMost workflows do not need an autonomous agent. They need a well-bounded procedure with a few intelligent steps.