Evals Are NOT All You Need
The Nuanced Perspective

Evals Are NOT All You Need


Summary

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.
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