When would you use deterministic checks, model graders, or human review?

Matches exact checks, model judgment, and human expertise to the behavior being measured instead of treating graders as interchangeable.

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TL;DR

I use deterministic checks for facts the application can verify exactly, such as schema validity, required citations, executable code, or permitted tool arguments. I use a model grader for scalable judgments with a clear scoring guide, such as relevance or whether a response addresses the request. Human experts remain necessary for ambiguous, high-impact, or newly discovered cases and to verify that automated graders still agree with the intended standard.

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Evaluator selection, cost, nuance, calibration, and layered scoring.

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The most reliable eval suites combine evaluator types because each one sees a different class of failure.
I use deterministic checks for facts the application can verify exactly, such as schema validity, required citations, executable code, or permitted tool arguments. I use a model grader for scalable judgments with a clear scoring guide, such as relevance or whether a response addresses the request. Human experts remain necessary for ambiguous, high-impact, or newly discovered cases and to verify that automated graders still agree with the intended standard.
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A structured-output workflow might first parse the response, validate its schema, and compare tool calls against allowed behavior before any model judges the explanation quality. Model grading makes broad regression runs affordable, but it can prefer longer answers or repeat the same blind spots as the system being evaluated. Blinded human review is slower, so I focus it on calibration samples, disagreements, risky slices, and changes near a release boundary.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Using an LLM to grade a condition code can verify exactly.
Assuming human reviewers always agree.
Treating a model score as ground truth.

When would you use deterministic checks, model graders, or human review?

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