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USING MODELS

Evals.

Evaluations · AI tests

Tests that measure how well an AI system performs specific tasks.

In plain words

Evals is short for evaluations: tests that give an AI system a task and judge its response or actions against defined success criteria.

A closer look

An eval combines a test case with a way to score the result. Grading can use code, human review, another model, or a combination. Useful test sets include realistic tasks and difficult cases.

Teams rerun evals when changing prompts, models, or tools to see what improved and what broke. Repeated trials help account for variation between runs. For agents, checking the actual outcome matters as well as reading the reply.

In practice

AN EXAMPLE

Before updating an AI writing assistant, a designer tests sample requests for shorter button labels. Each result is checked for length, preserved meaning, and tone, then compared with the previous version.

A useful distinction

Evals is shorthand for evaluation, not a separate technology. Passing a test set does not guarantee success everywhere; results depend on the cases and the quality of the grading.

Sources & further reading

Anthropic — Demystifying evals for AI agents (opens in a new tab)