AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
Also known as Evaluation
In plain terms
Evals are structured tests that measure how well an AI system performs on your specific task, using real examples and clear pass or fail criteria. Without evals, AI quality is a matter of opinion and every change is a gamble. They are how you know an update improved things rather than quietly broke them.
Evals are the quality control station at the end of the production line. Without them, you are shipping product and hoping customers find the defects instead of you.
A team builds evals using 200 real customer queries with expected answers. After every model change or prompt update, the eval suite runs automatically and no update ships if it drops below the quality threshold.
Before launch, after every significant change, and on a recurring schedule as the model or data drifts. Evals turn 'it seems to be working' into a measurable, defensible fact.
Evals only catch what they were designed to test. A strong eval suite for one task provides no coverage for a new task the system has been extended to handle. Expand evals when the system expands.
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