Glossary · AI & Agents
LLM Evaluation (Evals)
Overview
What LLM evaluation measures
LLM evaluation checks whether a model's responses meet the standards your use case requires. Common dimensions include correctness (is the answer factually right), relevance to the question, faithfulness to source documents (no hallucination), safety and tone, adherence to a required format, and operational factors like latency and cost. Different applications weight these differently, so teams define the criteria that matter before measuring.
How evals work
Teams build a test set of representative inputs with expected outcomes, then score model outputs automatically or with human reviewers. Metrics range from exact match and semantic similarity to rubric-based scores from an LLM acting as a judge. Evals run offline before shipping and continuously in production, catching regressions whenever a prompt, model, or data source changes.
Why evals matter for your business
Without evaluation, you cannot tell whether an AI feature is improving or quietly getting worse. Evals turn 'it seems to work' into measurable, repeatable evidence, which is essential before an assistant touches customers or revenue. When our team builds AI Agents, AI Chatbots, and AI Consulting projects, we pair every deployment with an eval set so changes can be validated, not guessed at.
Where we use it
Related Zen in Tech services
How our team puts LLM Evaluation to work in real projects.
FAQ
LLM Evaluation — common questions
Why do LLM applications need evaluation?
Because LLM outputs are non-deterministic and can regress silently when prompts, models, or data change. Evals give you objective, repeatable measures of accuracy, safety, and consistency so you can ship and update AI features with confidence rather than guesswork.
What is LLM-as-a-judge?
LLM-as-a-judge uses a separate language model to score another model's outputs against a rubric, such as helpfulness or faithfulness. It scales evaluation beyond manual review, though results are usually validated against human ratings to confirm the judge is reliable.
How is LLM evaluation different from traditional software testing?
Traditional tests check deterministic pass/fail logic; the same input always gives the same output. LLM evaluation measures quality on a spectrum because outputs vary, relying on datasets, similarity metrics, and human or model-based scoring rather than exact assertions.
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