Automated tests that grade LLM outputs against expected behavior.
1 min readupdated 2026-07-04
/ quick answer
Evals are the unit tests of AI systems. Each eval is an input, an expected property (exact match, LLM-judge score, latency budget), and a pass/fail. Ship without evals and you fly blind. Automated tests that grade LLM outputs against expected behavior.
Automated tests that grade LLM outputs against expected behavior. Evals are the unit tests of AI systems. Each eval is an input, an expected property (exact match, LLM-judge score, latency budget), and a pass/fail. Ship without evals and you fly blind. In practice: An eval suite runs 200 real support questions nightly; a regression alerts if grounded-answer accuracy drops below 92%. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Evals are the unit tests of AI systems. Each eval is an input, an expected property (exact match, LLM-judge score, latency budget), and a pass/fail. Ship without evals and you fly blind.
Example
An eval suite runs 200 real support questions nightly; a regression alerts if grounded-answer accuracy drops below 92%.
Evals are the unit tests of AI systems. Each eval is an input, an expected property (exact match, LLM-judge score, latency budget), and a pass/fail. Ship without evals and you fly blind.
What is an example of Evals?
An eval suite runs 200 real support questions nightly; a regression alerts if grounded-answer accuracy drops below 92%.
Why does Evals matter for AI and automation?
Automated tests that grade LLM outputs against expected behavior. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.