# LLM as a judge

URL: https://www.getdynamiq.ai/glossary/llm-as-a-judge

> Using a large language model to score another model's output against a written rubric, in place of, or alongside, a human reviewer.

Using a large language model to score another model's output against a written rubric, in place of, or alongside, a human reviewer.

An LLM-as-a-judge metric is a model given a written rubric and asked to score an answer against it, returning a structured score rather than free-form commentary. It is best suited to qualities that are hard to check with code: tone, relevance, completeness, whether an answer actually reflects its source material.

Regulated teams often need to check far more agent responses for quality and policy adherence than a human review team can read one at a time. A judge model applying the same written rubric to every sampled response is what makes that review scale without lowering the bar on any single check.

A rubric scores whether a support reply reflects the retrieved policy passage it was supposed to be grounded in, applied consistently to every conversation sampled for review, not just the ones a person happens to read.

**In Dynamiq**, an LLM-as-a-judge metric is defined once, with a chosen judge model and a rubric written using placeholders such as question, answer and context, then reused across evaluation runs and online evaluations. Every score stays tied to the exact rubric version that produced it, so a later rubric change never rewrites history.

## See it in Dynamiq

-   [Evals](https://www.getdynamiq.ai/product/evaluations)

## Related terms

-   [LLM evaluation](https://www.getdynamiq.ai/glossary/llm-evaluation)
-   [Online evaluation](https://www.getdynamiq.ai/glossary/online-evaluation)
-   [Hallucination](https://www.getdynamiq.ai/glossary/hallucination)

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