AI Evaluation and Quality Assurance That Holds Up

Define the intended behaviour

State the task, acceptable output, prohibited output, user, context, consequence, human checkpoint, and evidence of completion.

The Quality Assurance and Testing route provides an internal path when evaluation needs wider test and release discipline.

Build representative evaluation cases

Include normal, ambiguous, incomplete, restricted, conflicting, and high-consequence cases. Record expected behaviour, reviewer, test basis, and acceptance condition.

Test failures, uncertainty, and misuse

Test wrong context, unavailable tools, stale sources, prompt changes, access failures, unexpected input, and repeated attempts. Define whether the workflow clarifies, refuses, escalates, or stops.

Record reviewer decisions and evidence

Retain cases, outputs, reviewer decisions, material changes, exceptions, and unresolved limitations. Make the release decision reproducible.

Connect evaluation to release and support

Define pilot and release conditions, support path, monitoring signal, disablement action, and re-evaluation trigger.

Questions teams ask about AI quality

One accuracy number is not enough. Evidence depends on the task, consequence, error types, human review, data, and operating context.