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LLM-as-a-Judge

An evaluation method where one LLM scores the output of another against a rubric, used to grade AI quality at scale without a human rating every response.

What Is LLM-as-a-Judge?

Manual review does not scale past a few hundred outputs. LLM-as-a-Judge prompts a strong model to score responses for relevance, faithfulness, and tone against an explicit rubric, producing a numeric signal you can track per release. It is not perfect: judges carry their own biases and need calibration against a human-labelled gold set. Used well, it turns AI quality into a metric that gates deploys the same way unit tests gate code.

How Groovy Web Uses This

We build LLM-as-a-Judge pipelines into client AI products so quality is measured every release, calibrated against a human-rated sample. It is a core part of how we run evaluation-driven AI engineering.

Related Terms

Agent Evaluation AI Hallucination Eval-Driven Development LLM Evals

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