AI evals
Systematic test suites for AI systems — input/expected-output pairs run automatically to catch regressions when models or prompts change.
AI evals are the agent equivalent of unit tests. You define a set of inputs (prompts, scenarios, tool-use traces) and the expected behavior, then run the suite on every model swap, prompt change, or agent update to catch regressions before they hit production.
Modern eval stacks combine three layers: exact-match checks for deterministic outputs, LLM-as-judge for open-ended responses, and human review for edge cases. Tools like Braintrust, Promptfoo, and LangSmith have made evals a one-command CI step.
In 2026, "we have evals" is still a real differentiator. The teams that ship reliable agents always have them; the teams that ship flaky agents almost never do. Start with 50 hand-curated test cases for your top intents — that beats 500 auto-generated ones.
Frequently asked
What is the difference between AI evals and traditional unit tests?+
Unit tests check deterministic logic; AI evals check probabilistic outputs. Most evals use a stronger LLM as a judge or human review because exact-match fails when valid outputs differ.
How many eval cases do I need?+
For a focused agent, 50–200 hand-curated cases covering top intents and known failure modes is usually enough. Past 500 cases marginal value drops sharply unless you have automated case generation.
Should evals run in CI?+
Yes — gate every model swap or prompt change behind the suite. Most teams run a small "smoke" eval on every commit and the full suite on releases.