Evals are the new unit tests: a practical guide

Every team that ships an AI feature eventually hits the same wall: a small prompt change fixes one bug and quietly creates three more. Without evaluations, you only find out when users complain.

Why evals matter

Unit tests give engineers confidence to refactor. Evals give AI teams the same confidence to change prompts, swap models or add tools. They turn "it feels better" into a number you can track.

If you can't measure a change, you're not iterating — you're guessing.

Building your first eval set

Start with real traffic, not synthetic examples. Pull 200–500 conversations from production and label what a great answer looks like.

1. Sample conversations across intents and difficulty.
2. Write a reference answer or a rubric for each.
3. Pick scorers: exact match, LLM judge or custom code.

nv eval run --suite support-v1 --compare v4.1,v4.2

Gating releases

Once you trust your scores, wire them into CI. A pull request that drops accuracy below your threshold simply doesn't merge. Use nv eval gate --min 0.92 in your pipeline.

What about cost?

A 500-example suite with an LLM judge costs a few cents per run. That's cheaper than one bad answer reaching a customer.

Next steps

Read our evaluation docs or start a free workspace and run your first suite in ten minutes.