Author(s): Rohan Mistry

Originally published on Towards AI.

Calibrate your judge. Detect its biases. Trust your scores.

Your LLM judge might be lying to you.

After introducing the problem of uncalibrated LLM evaluators, the article explains what LLM-as-a-judge actually is (a model scoring another model’s output using criteria/rubrics), why teams use it instead of human review or string/code checks, and the three common judging modes—single-output scoring, pairwise comparison, and reference-based scoring—arguing that pairwise is usually the best default. It reviews evidence that judges can align with human preferences only when properly calibrated for the relevant task, then details the key biases that must be designed around (like position bias and self-preference). The core implementation section provides a practical nine-step process: start from real failure modes, limit criteria, use binary decisions, force reasoning before verdicts, add few-shot anchors, decompose subjective judgments into sub-decisions, pin configuration, calibrate against human labels with proper agreement metrics, and ensemble when stakes are high. It concludes with monitoring/validation practices (“judging the judge”), where to place judges in CI/regression gates, pre-release comparisons, and production monitoring, when not to use a judge (when deterministic checks, cheap ground truth, or specialist knowledge applies), and the takeaway that an LLM judge is a small ML system that must be built, calibrated, versioned, and continuously validated.

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Published via Towards AI