Before a model sees the data
Human review can shape collection rules, normalize inconsistent inputs, apply taxonomies, and decide what should be excluded. That preparation influences every downstream evaluation, so unclear source data is often an operating problem before it becomes a model problem.
While outputs are being evaluated
Comparisons and scores become useful only when reviewers share the same interpretation of the rubric. Calibration examples, disagreement review, and documented edge cases turn individual opinions into a repeatable evaluation system.
At exceptions and escalation points
Automation is most valuable when routine work moves quickly and unusual cases are surfaced clearly. A human review layer should have explicit triggers, a defined decision path, and a record of what happened next.
After deployment
Sampling real outputs can reveal new failure patterns, changing user behavior, or gaps that were absent during initial testing. Human monitoring converts those observations into updated rules, retraining material, and better escalation logic.
