Product Evals in Three Simple Steps
Eugene Yan
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.
Why it matters
Label some data, align LLM-evaluators, and run the eval harness with each change.
