How to Create an LLM Judge That Aligns with Human Labels
SMRTR summary
An AI system for evaluating code review quality was developed and tested. The process involved defining criteria, labeling data, crafting prompts, and comparing AI judgments to human expert labels. Through iterative refinement, the system achieved 98% accuracy in matching human assessments of code review helpfulness and tone. Different LLM models and providers were tested, highlighting the importance of prompt engineering and model selection.
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