What Are AI Evals? A Practical Guide to Measuring Capability, Safety, and Reliability

This explainer covers the current state of AI evaluation frameworks, describing how engineering teams design and run evals to measure model capability, safety alignment, and production reliability. It walks through different eval categories — including behavioral benchmarks, red-teaming protocols, and automated regression testing — and explains how they fit into model development and deployment pipelines. The piece is particularly useful for teams that are adopting third-party models and need to build their own eval harnesses to validate that a model meets their specific application requirements. As model providers ship rapid updates, having a robust internal eval suite is increasingly the difference between catching regressions before they reach production and discovering them through user complaints. Developers new to evals will find this a solid orientation; experienced teams may use it to benchmark their current practices against emerging norms.