Evaluation/Evals
Systematic measurement and evaluation of AI quality.
Explanation
Evals are tests and metrics used to measure the quality of AI outputs. They help to identify whether a system works reliably and where weaknesses lie.
How it works
You define test cases with expected results and let the AI process them. Automatic and manual ratings show how correct and helpful the answers are.
Example
Define 100 typical customer requests as a test set. The AI agent answers them and the results are checked for correctness, tonality and completeness.
Why it matters
Without Evals you fly blind. Systematic evaluation is the basis for continuous improvement and trust in AI systems.
What is an eval? Meaning and types
An eval, short for evaluation, is a systematic test for an AI system. Because a language model does not always answer the same question the same way, and because there are often several acceptable answers, classic software tests are not enough on their own. Evals measure how well a system performs across many representative cases.
Three types of grading are usually combined. Code-based checks verify anything unambiguous automatically: is the format valid, is the amount correct, was the right source cited? LLM-as-a-judge uses a second model to score answers against defined criteria such as completeness or tone. Human review by domain experts remains the reference, especially early on and for spot checks.
How to build a good eval set
Start with a test set of real cases with known correct outcomes, often called ground truth. 50 to 100 carefully chosen cases are a solid start, including deliberately hard edge cases and questions the system should answer with “I don't know”.
The value shows with every change: a new prompt, a new model, a different chunk size. Instead of deciding by gut feeling, you run the test set and see whether the system got better or worse. In production, online evals complete the picture with user feedback, sampled reviews and the share of cases escalated to humans.
Frequently asked questions
What does evals mean in AI?
Evals is short for evaluations: structured tests and metrics that measure the quality of an AI system, usually across a set of example cases with known correct results.
What is the difference between evals and benchmarks?
Benchmarks are public, standardized tests used to compare models in general. Evals test your own application with your own data and requirements, which makes them far more useful for business decisions.
When should you start writing evals?
As early as possible, ideally during the pilot. A small set of real cases prevents improvements that are only felt rather than measured, and it makes later model switches manageable.