Definition
Evals, short for AI evaluation, are structured tests that measure how well an AI system performs on the tasks it is meant to do. An eval consists of a set of representative inputs, the expected outcomes or grading criteria, and a way to score the results, whether by exact match, by human review or by another model acting as judge. Evals turn "the AI seems fine" into a number that can be tracked, compared and defended.
In a company, evals are what separate an AI demo from a product you can trust. Before launching a support assistant, the team builds a set of a few hundred real questions with approved answers and measures accuracy, tone and safety. Every change to the prompt, the model or the retrieval pipeline is re-run against that set. Evals also run in production on samples, catching regressions when a vendor updates a model.
Evals have become the core engineering discipline of applied AI, with public benchmarks for models and private evals for products. Using a strong model to grade another model's output is standard, with human checks to calibrate the grader. The misconception is that evals are a one-off acceptance test. They are a living asset that grows with every failure found, and they are the only honest way to know whether a change helped.
In practice
Before switching its assistant to a cheaper model, a company ran its 400-case eval suite. The new model scored equal on accuracy but worse on refund policy questions, so the team fixed the prompt first and saved the cost without a customer noticing.
Why it matters
If your team cannot show you an eval score, they do not know how good the AI is. Evals are how you manage AI quality the way you manage any other KPI.
Frequently asked questions
- What are evals in AI?
- Evals are test suites for AI systems: a collection of realistic inputs with expected outputs or grading rules, run automatically to score accuracy, safety and quality. They are used to compare models, validate prompt changes and catch regressions, the way software tests protect code.
- How do you evaluate an LLM application?
- Collect real examples of the task with approved answers, define what good looks like, score outputs automatically where possible and with a strong model or human graders where judgement is needed, then run the suite on every change. Add every production failure to the set so it keeps improving.