What is LLM-as-a-judge, and when can you trust it?
LLM-as-a-judge uses one language model to score the output of another against a rubric — is this answer relevant, grounded, complete, safe. It scales evaluation past what humans can read by hand. You can trust it when you've calibrated it against human judgments on your own data, given it a concrete rubric, and kept a person in the loop for the high-stakes calls. Used blind, it inherits the same biases as the model doing the grading.
AI Evaluation & ReliabilityAI Observability
The problem it solves
You cannot have humans read every response an AI system produces in production. There are too many, and they arrive too fast. LLM-as-a-judge fills that gap: a second model reads each output and scores it against a rubric — relevance, factual grounding, completeness, tone, safety — so you get a quality signal on everything, not just a sample.
Why a rubric matters more than the model
A judge is only as good as what you ask it to check. “Rate this 1-10” produces noise. A judge that checks specific, named criteria — “is every claim supported by the retrieved context,” “does it answer the question that was asked” — produces a signal you can act on. The rubric is where your definition of “good” actually lives.
Where it breaks, and the guardrail
A judge model carries its own biases. It can favor longer answers, prefer its own phrasing, or wave through a fluent answer that is wrong. The fix is calibration: score a batch by hand, compare to the judge, and adjust until they agree on your data. Then keep humans on the decisions that matter most. The judge handles volume; people handle the cases where being wrong is costly.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
