What is LLM evaluation? A practical guide to evals, metrics, and regression testing

LLM evaluation is a crucial process for ensuring the quality and reliability of LLM-powered applications by systematically measuring their performance against defined criteria. It involves both offline and online evaluation modes to test changes before deployment and monitor live production traffic for unanticipated issues, respectively. The evaluation process is not limited to assessing the entire system but also includes component-level checks to identify specific sources of failure, such as prompt changes, retrieval quality, and generation accuracy in RAG pipelines, as well as safety compliance. Effective LLM evaluation requires building a comprehensive workflow that includes dataset construction, rubric definition, evaluator selection, scoring, and CI/CD integration to automate the process and prevent regressions. Additionally, organizations can benefit from platforms like Braintrust, which offer integrated evaluation infrastructure, enabling teams to conduct systematic evaluations, manage datasets, trace failures, and monitor production performance, ultimately leading to more stable and user-aligned systems.

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