AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
2 min readupdated 2026-08-01
/ quick answer
An eval has three parts: a dataset of inputs with expected outcomes, a scorer (exact match, assertion, or model-graded rubric), and a recorded score over time. Without evals, every prompt or model change is a guess. The dataset should grow from real production failures, not invented examples.
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against. An eval has three parts: a dataset of inputs with expected outcomes, a scorer (exact match, assertion, or model-graded rubric), and a recorded score over time. Without evals, every prompt or model change is a guess. The dataset should grow from real production failures, not invented examples. In practice: 40 stored support tickets with expected tags and required facts; any prompt change must keep the pass rate above 92%. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
An eval has three parts: a dataset of inputs with expected outcomes, a scorer (exact match, assertion, or model-graded rubric), and a recorded score over time. Without evals, every prompt or model change is a guess. The dataset should grow from real production failures, not invented examples.
Example
40 stored support tickets with expected tags and required facts; any prompt change must keep the pass rate above 92%.
An eval has three parts: a dataset of inputs with expected outcomes, a scorer (exact match, assertion, or model-graded rubric), and a recorded score over time. Without evals, every prompt or model change is a guess. The dataset should grow from real production failures, not invented examples.
What is an example of AI Evaluation?
40 stored support tickets with expected tags and required facts; any prompt change must keep the pass rate above 92%.
Why does AI Evaluation matter for AI and automation?
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.