Product Evals in Three Simple Steps
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A practical three-step guide to building product evaluations for LLM-based systems: (1) label data using binary pass/fail labels with at least 50-100 failure cases, preferring organic failures over synthetic ones; (2) align LLM-evaluators per dimension using a dev/test split, accounting for position bias and measuring with precision, recall, and Cohen's Kappa; (3) build an eval harness that integrates with the experiment pipeline for fast iteration. Key insights include why binary labels beat Likert scales, how to determine sample sizes using confidence intervals, and a real-world anecdote showing how 4 weeks of eval infrastructure enabled hundreds of experiments in the following months.
