Using LLM-as-a-Judge For Evaluation: A Complete Guide
3.78T1.5 sourcehamel.dev (Hamel Husain)
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Published by hamel.dev (Hamel Husain) (T1.5 source). The original is at https://hamel.dev/blog/posts/llm-judge/.
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The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA practitioner's guide to using LLMs as judges for evaluating AI outputs, based on experience with 30+ companies. It identifies common pitfalls such as excessive metrics, uncalibrated scoring scales, and ignoring domain experts, then introduces a step-by-step 'Critique Shadowing' method beginning with identifying a Principal Domain Expert.
Why it mattersConcrete evaluation framework grounded in cross-company consulting experience. The Critique Shadowing method gives a specific alternative to dashboard-driven eval approaches that produce untrusted metrics.
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