Evaluation of AI Usage

In our previous lessons, we’ve built small AI applications that utilize text, images, audio, embeddings, and various tools. But how do you know if your AI system is actually working correctly? How do you measure if changes to your prompts make things better or worse? This lesson introduces evaluations, that is, the systematic testing of AI applications to ensure that they meet your quality standards.

By the end of this lesson, you’ll know how to create comprehensive tests for your AI applications, measure their performance objectively, and iteratively improve them with confidence.

Why evaluations matter?

Building AI applications without evaluations is like coding without tests; you might think everything works, but you have no way to verify it or catch regressions when you make changes.

AI outputs can be subjective and varied. The same prompt might work perfectly 90% of the time, but fail catastrophically on edge cases. Without systematic testing, you won’t discover these failures until your users encounter them.

What do evaluations solve?

• Quality assurance: Verify that your AI meets performance standards before deployment.

• Regression detection: Catch when changes accidentally strain existing functionality.

• Prompt optimization: Compare different approaches objectively with data.

• Model comparison: Test which models work best for your specific use cases.

• Confidence in changes: Make improvements knowing you won’t compromise on what already works.

Evaluations follow a three-step process similar to behavior-driven development (BDD).

1. Define expected behavior: Describe what good output looks like.

2. Test with real data: Run your AI on representative examples.

3. Analyze and improve: Use results to refine your approach.

Instead of hoping your AI works correctly, you create measurable criteria for success and test against them systematically.

How to evaluate with the OpenAI API

Let’s build a complete evaluation from scratch. We’ll create an AI system ...