A model that grades another model does not automatically become a reliable judge of business quality.

Start with a clear rubric describing acceptable evidence, important errors, and what reviewers should do with an uncertain result.

Compare machine judgments with human decisions on difficult cases, especially disagreements, rather than calibrating only on obvious successes.

Protect the evaluator's inputs and keep a blinded sample. Continue human sampling when models, documents, or judging instructions change.

Use scores to focus attention and triage review. A precise number is useful only when its meaning and limitations are understood by the people making the decision.

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