THE SHORT ANSWER
What should you measure before releasing an AI feature?
Evaluate task quality, end-to-end latency, failures, and cost per accepted result on representative requests. Include edge cases and escalation paths, and compare model or prompt changes on the same held-out cases. Set release criteria with the workflow owner and keep monitoring after launch.
Build a representative test set
Include normal requests, difficult edge cases, and inputs the system should refuse or escalate. Use data you are allowed to test with. Keep examples separate from prompt tuning so the evaluation can expose regressions.
Define an acceptable result
For document search, check whether the answer is supported by the retrieved source. For extraction, compare individual fields with a reviewed answer. Record where human judgment is needed instead of relying on one overall score.
Measure the whole request
Track end-to-end latency, failures, retries, and cost per accepted task. Include retrieval and application infrastructure as well as model usage. Compare models or prompts on the same cases before making a cost or quality decision.
A starting checklist
Set release criteria with the workflow owner.
Record the model, prompt, and retrieval version.
Compare quality and cost together.
Prepare monitoring and a rollback path.
Continue evaluating after launch. Real usage can reveal tasks and failure patterns that were absent from the original test set.
