Brief
Guide Introduces 'Critique Shadowing' for LLM Evaluation
The guide 'Using LLM-as-a-Judge For Evaluation: A Complete Guide' describes a technique called Critique Shadowing, which aims to help teams avoid common pitfalls like too many metrics and arbitrary scoring systems.
The guide identifies common mistakes teams make when using LLMs to evaluate AI outputs: creating too many metrics, using arbitrary 1-5 scoring systems, ignoring domain experts, and using unvalidated metrics. The proposed solution is a technique called 'Critique Shadowing.' The first step is to find the principal domain expert, whose judgment is crucial for the AI product's success.
Source details and supporting facts
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Stated by hamel.dev
- The author wrote 'Your AI product needs evals' earlier this year.
- The author helped over 30 companies set up evaluation systems.
- Common mistakes include too many metrics, arbitrary scoring systems, ignoring domain experts, and unvalidated metrics.
- The solution is called 'Critique Shadowing'.
- Step 1 is to find the principal domain expert.
Sources
- Hamel HusainText stored 15 September 2026
How this story was checked. Written from the 1 page listed above, stored 15 September 2026; claims checked against that stored text on 15 September 2026.
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