Building eval systems that improve your AI product

In this episode of Lenny's Reads, Lenny presents an audio version of a post by Hamel Husain and Shreya Shankar about building effective AI evaluation systems. The episode focuses on a three-phase playbook: discovering what to measure through error analysis, building a reliable evaluation suite, and operationalizing the suite for continuous improvement. It emphasizes grounding evaluations in real user problems, using both code-based and LLM-as-a-judge evaluators, and tailoring evaluation strategies for different AI architectures like multi-turn conversations, RAG pipelines, and agentic workflows. The goal is to create evaluation systems that are trusted and drive real product improvements.