When should I use an LLM judge instead of a deterministic check?

Reach for an LLM judge when grading needs judgment: whether an answer actually addresses what was asked, whether a tool call was justified given the context, whether a multi-step plan held together. A rule or a classifier doesn’t answer those reliably, because the criterion is a reading of intent against context, not a fixed pattern.

Skip it when a deterministic check exists instead. A schema violation, a missing field, or a bad status code is faster and cheaper for code to catch, and a judge call only adds cost and stochastic noise on top of that. A judge is also the most expensive grader per verdict, so most teams don’t run it on every output: a fixed dataset before a change ships, and a sampled or flagged slice of production traffic rather than the full stream.

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Did my agent get worse, or did my judge?You can't tell from a falling score alone; run a fixed, human-labelled anchor set through the current judge on a steady interleave and watch it separately.answer →Does an LLM judge know when it's unsure?Somewhat: a judge's self-reported confidence predicts its own errors better than sampling it five times and voting, at a fifth of the inference cost.answer →Does pinning the judge model version stop it from drifting?Not fully. A pinned version string is a record, not a guarantee, since a hosted provider can serve updated weights under the same stable alias.answer →Everything on LLM as judgeThe definition, and every question answered under LLM as judge.concept →

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