Product Evals in Three Simple Steps

Eugene Yan · 7 months ago

The article outlines a three-step process for building product evaluations for LLM systems: labeling a small dataset with binary pass/fail or win/lose labels (aiming for 50-100 failure cases), aligning individual LLM evaluators to single criteria using 75% of samples for development and 25% for testing, and running an evaluation harness integrated with experiment pipelines to assess configuration changes. The key concrete benchmark is achieving Cohen's Kappa scores of 0.4-0.6 for substantial agreement and 0.7+ for excellent inter-annotator reliability, with target sample sizes determined by statistical confidence intervals (e.g., 400 samples for ±1.7% margin of error). This approach enables teams to iterate rapidly through dozens to hundreds of experiments per cycle rather than being bottlenecked by manual human annotation after each change.