Build a Calibrated Rubric for AI Output Quality
Build a rubric that calibrates human and model judgment for AI output testing.
A support chatbot eval uses a 1 to 5 quality score, but reviewers disagree because quality is not defined. Define criteria, write anchors, calibrate reviewers, and set pass-review-fail thresholds. The common trap is asking for a quality score without anchors. That creates a dashboard number that changes with reviewer mood, judge prompt wording, or examples seen earlier in the day. Select criteria Use four criteria tied to risk: source grounding, policy correctness, actionability, and professional tone. Criteria should explain why the answer matters. Avoid broad labels like good unless they are decomposed into observable signals. Write anchors Create pass, fail,…
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