Model Evals

Model evaluations (evals) are the AI equivalent of automated test suites — structured assessments that measure whether an AI system is behaving as expected across a range of inputs and scenarios. A basic eval consists of: a dataset of test inputs with known expected outputs or evaluation criteria, a procedure for running those inputs through the AI system, and a scoring mechanism that assesses the quality of each output against the criteria. Evals range in complexity from simple factual accuracy checks (did the model correctly answer this question from the documentation?) to nuanced quality assessments (is this response appropriately helpful, accurate, and within policy?) that themselves may require a separate AI model as the evaluator (called an LLM-as-judge approach). Well-designed eval suites cover the task distribution the system will encounter in production, including known hard cases and adversarial inputs.

The critical use of evals is regression detection: as model providers release new versions, prompts are modified, and retrieval pipelines change, eval suites catch whether the system's performance on the tasks that matter has improved or degraded. Without evals, teams discover performance regressions through user complaints and production incidents — after the damage is done. With evals, changes can be tested against a comprehensive behavioral baseline before deployment, making AI development iterative and data-driven rather than speculative. Building evals requires an upfront investment in defining what good outputs look like (harder than it sounds for open-ended tasks) and collecting or creating representative test datasets, but this investment pays compound returns as the AI system is iterated on over time.

For B2B teams, evals are the non-obvious engineering investment that separates teams that ship reliable AI products from teams that are constantly fighting fires. The practical starting point is low-effort: collect 50-100 representative real user queries from your application, manually label the expected good responses or quality criteria, and run the eval suite before each significant change (new model version, major prompt change, retrieval pipeline update). Even a small eval set that exercises the core tasks catches the majority of meaningful regressions. As the system matures, invest in expanding the eval set to cover more edge cases, add adversarial inputs, and potentially move to LLM-as-judge scoring for tasks where manual labeling at scale isn't feasible. The eval infrastructure is the foundation that makes everything else in AI development reliable.