Evals Are NOT All You Need

This article argues that focusing solely on “evals” (traditional AI model evaluation) is insufficient for ensuring AI product quality. Instead, teams should build a continuous “flywheel” system—monitoring production data, identifying new failure cases, updating metrics, and iteratively improving the product—to address the unpredictable nature of real-world user interactions. The authors emphasize the distinction between evaluating the underlying model and assessing the performance of the product built on top of it, advocating for a dynamic, data-driven approach to quality assurance rather than static benchmarks or checklists.