Eval-driven development: Lessons from evaluating GenAI at scale

This post extends the LLM Evaluation Infrastructure capability by detailing the practical application of Eval-driven development (EDD) for building trustworthy Generative AI products at scale. It elaborates on the five core principles of EDD, introduces a three-layer evaluation methodology (programmatic checks, LLM-as-a-Judge, and human evaluation) with specific examples and calibration techniques for virtual judges, and outlines a detailed approach for evaluating agentic systems by analyzing step, trajectory, and session levels using observability data. A walkthrough of a support policy AI assistant evaluation is provided.