A teammate asks what 'running evals' means in the LLM context. How would an engineer explain it without jargon?

**LLM evaluation** is the practice of systematically measuring how well a language model performs on tasks that matter to your application. Instead of reading a few outputs and deciding they look okay, you build a repeatable process. The process has three parts. First, you assemble a test set of representative inputs, ideally ones that cover the edge cases your users will hit. Second, you define how to score each output. That might be an automated metric like ROUGE for summarization, an LLM judge that rates responses on a rubric, or a human annotator doing pairwise comparisons. Third, you run the model, collect scores, and compare them to a baseline. The baseline is what makes evaluation useful. It could be yesterday's prompt, the previous model version, or a competitor. Without a baseline, a score of 72% means nothing because you have no way to know if that is good or bad for your use case. Teams that skip evaluation end up shipping changes that feel better on a few cherry-picked examples but actually regress on the long tail. Evaluation turns subjective impressions into measurable evidence, and that evidence is what lets you move fast without breaking your product.

**LLM evaluation** is a structured process for measuring model quality on a defined set of tasks, using metrics, reference answers, or human and LLM judgments to produce reproducible scores. The simplest version has three ingredients. A test set: a curated collection of inputs that represent the workload your model will face in production. A scoring function: something that looks at each output and returns a number. And a comparison point: a baseline score from the previous model version, prompt, or pipeline so you can tell whether a change helped or hurt. Scoring can happen three ways. Automated metrics like BLEU, ROUGE, or `pass@k` compute a number from the text alone. LLM-as-judge evaluation hands each output to a strong model like Claude Opus 4.7 with a rubric and collects its rating. Human evaluation asks annotators to score or rank outputs, producing the highest-quality signal but at the highest cost. The reason evaluation matters is regression detection. Without evals, every prompt tweak or model swap is a gamble. With evals, you know exactly which capabilities improved and which degraded. In production teams, evals run automatically in CI so that no change ships without evidence that it did not make things worse. The one-liner for your teammate: evals are the automated quality gate that turns 'it looks fine' into 'here are the numbers.'