Human review is still essential for many AI systems—especially for tone, helpfulness, safety nuance, and domain appropriateness. But human review fails when it is unstructured: reviewers disagree, scores drift, and results don’t translate into engineering decisions.
A good rubric turns qualitative judgement into repeatable signals that can guide releases and regressions.
Define the dimensions that matter
Most rubrics work best with 4–6 dimensions such as correctness, grounding, safety, usefulness, and tone.
Use anchors and examples
Provide example responses for “excellent”, “acceptable”, and “fail” so reviewers calibrate quickly.
Connect rubrics to release decisions
Use rubrics as part of evaluation gates (see evaluation loops) and canary releases (see canary rollouts).