LLM evaluation is how teams measure whether a large language model actually does what they need. Instead of judging a model on a single answer, evaluation runs it against defined test sets and scores the outputs on dimensions such as accuracy, relevance, factual grounding, safety, and consistency. Methods range from automatic metrics and reference comparisons to human review and model-graded scoring, often called LLM-as-a-judge.
Evaluation matters most when a model moves from a demo into production, where the same prompt should return dependable results over time. A single benchmark number means little if the test data or run conditions shift between evaluations. Reliable evaluation therefore rests on AI-ready data in a fixed, versioned state and on reproducible runs, so a score can be compared and trusted rather than rebuilt from scratch each time.
Related terms: Model Evaluation · LLM Observability · Ground Truth · Reproducibility · Reproducible AI Execution