Evals 101: How to Know If Your AI Actually Works

AI can give you a great result once and a mediocre one five minutes later. The input changes slightly. The context shifts. The model makes a different choice. Suddenly the prompt, skill, or workflow that felt reliable breaks, and you cannot explain why.

An eval gives you a way to test it. You define the task, collect examples that reflect the real work, write down what good looks like, and grade the results against that standard. It turns “this seems good” into a test you can run again.

Once you turn a prompt into a skill, workflow, or agent, you have built a software system. Nobody ships you its quality bar. You have to create it.

In this session, I’ll break down how to turn a real AI task into an eval. We’ll define success, choose test cases, grade the output, decide what counts as good enough, and use failures to improve the system.

You’ll leave knowing how to test AI work, spot when quality slips, and decide what to improve next.

This is for you if:

• You understand the idea of evals, but building one still feels like engineering work.
• You have built prompts, skills, workflows, or agents and need to know whether they are reliable.
• Your team keeps debating whether AI can handle a task and needs a repeatable way to test it.

What you'll get:

• A repeatable method for turning an AI task into an eval.
• A complete example you can adapt to your own work.
• Flowcharts for choosing test cases, grading results, and deciding what to fix when something fails.

See you Friday. I'll make evals make sense.