If you’re working with AI models especially large language models you’ve probably had that moment you run a prompt, it sounds great… but the output feels off. Maybe it misunderstood the context. Or labeled something incorrectly. Or just wasn’t consistent.

That’s the thing about AI, it works great until it doesn’t.

And that’s exactly why evaluations (aka evals) matter.

So, what are “evals” anyway?

Think of evals like mini pop quizzes for your AI. You’re not testing how smart the model is but you’re testing whether it can consistently do the specific thing you need it to do.

OpenAI’s Evals API gives you a structured way to Test your AI:

1. Define what you expect the model to do

2. Feed it real or simulated data

3. Score how well it performs, based on clear, measurable criteria

Why should you care?

Because every time you update your prompt, switch to a new model, or scale your product—you risk breaking something that used to work. Without testing, you might not even realize it.

Evals help you:

1. Catch mistakes before they hit users
2. Avoid embarrassing or incorrect outputs
3. Build trust with your team and your customers
4. Sleep better at night knowing your model is actually doing its job

If you’re building anything with LLMs chatbots, content generators, code helpers, you name it, you need a system for checking whether your model’s output actually meets your expectations.

OpenAI’s Evals API makes that process painless, flexible, and kind of satisfying.

Because great AI isn’t just about cool outputs.

It’s about reliable, measurable, trustworthy performance.

Want to try it?

Check out OpenAI’s guide to Evals or play around with your own data in the OpenAI dashboard.