Evals for Everyone: A Deep Dive

This article summarizes a three-part series called “Evals for Everyone” focused on comprehensive AI evaluation. The series highlights the confusion around the term "evals" – often meaning different things to different teams – and emphasizes the importance of aligning evaluation with product goals, not just model benchmarks. Key takeaways include starting evaluations with observed failures and prioritizing impactful, reliable, and cost-effective metrics, alongside a framework for scaling evaluations and utilizing both code-based and LLM-based judging with a focus on explainable results for effective debugging.