AI systems are inherently non-deterministic, leading to challenges in assessing improvements and ensuring quality without a systematic evaluation process. Evals provide a structured method to address these issues by offering statistical confidence in changes, catching regressions, and facilitating continuous improvement. The evaluation framework in Braintrust is built on three components: task, dataset, and scores. The task defines what is being evaluated, the dataset consists of real-world examples to uncover unexpected issues, and scores measure various performance dimensions. Effective evaluation requires clear success metrics, a broad-to-narrow evaluation approach, and a focus on one dimension per score. Integrating production feedback into evaluations helps identify and prevent recurring issues, creating a feedback loop that continuously enhances AI performance. By adopting this systematic approach, teams can confidently deploy AI products, addressing user complaints as test cases and validating feature ideas before release, thus shifting from uncertain development to data-driven improvements.