AI glossary · Building and running AI
AI evals
AI evals are repeatable tests that check whether an AI feature gives good results, using set inputs and clear scoring rules.
Also called evaluations, LLM evaluation, model evaluation
In more detail
Teams building AI products collect real examples, decide what a good answer looks like and score the model’s outputs, by hand, with code or with another model as a judge.
Evals show whether a change to a prompt, model or setting made things better or worse. Unlike public benchmarks, they reflect your actual use case.
Related terms
AI benchmarkAn AI benchmark is a standard test used to measure and compare how well AI models perform on a task, such as maths problems, coding or general knowledge.Prompt engineeringPrompt engineering is the skill of writing and refining prompts so an AI gives more accurate, useful and consistent results.AI hallucinationAn AI hallucination is when an AI confidently gives an answer that is false or made up, such as a fake fact, quote or source.
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