evals▌

Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.

Systematic evaluation framework for AI products using practitioner-driven methodologies. \n \n Guides users through understanding what \"good\" looks like, designing rubrics and test cases, and implementing scoring criteria aligned with actual user needs \n Emphasizes manual review and error analysis as prerequisites to building meaningful evals, with structured workflows for clustering failure patterns \n Flags common pitfalls including vague criteria, LLM-as-judge without validation, and Liker