What Are AI Evals? How They Work and How to Run One

AI evaluations are scored testing systems designed to measure the behavior of nondeterministic AI applications, where conventional pass-or-fail assertions may miss regressions such as hallucinated facts or degraded multi-turn responses. They combine a versioned dataset of realistic inputs, a task, graders that assess outputs from 0 to 1, and release thresholds that compare candidate performance with a baseline. Unlike public benchmarks, which measure broad model capabilities, evals focus on an organization’s specific application, policies, users, and production risks. Common forms include model, prompt, retrieval, and agent evaluations, with agent testing also assessing tool use, trajectories, recovery from errors, and user outcomes. Effective programs use deterministic code-based graders for clear rules and model-based graders for subjective qualities, validate automated judges against human labels, account for run-to-run variance, and run continuously after prompt, model, or retrieval changes. The material emphasizes that aggregate scores alone can conceal serious failures, so release gates should also prevent regressions in individual and critical cases, while datasets should grow from real incidents and production observations over time.

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