Evaluation: knowing whether it actually works
Ordinary software either compiles or it does not. AI systems have no equivalent. The answer is always plausible, which means the only way to know whether something is good enough to put in front of a real person is to measure it against a standard you defined in advance. If you read one lesson in this course carefully, read this one.
Design a test set for an AI system, work out which component is causing a bad answer, and tell the difference between a real improvement and noise.
Lessons 1 to 3. The diagnostic sections assume you know what retrieval and agents are.
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Each section opens with a short summary before the detail. Turn this off once the vocabulary feels familiar.
Nothing tells you an AI answer is wrong. It always looks right. So the only way to know whether a system works is to test it against answers you already know, decided in advance.
When ordinary software breaks, it usually tells you. It throws an error, or fails a test, or refuses to run. An AI system that is wrong produces a fluent, confident, well-structured answer that looks exactly like a right one. There is no signal. That is the whole problem, and it is why measurement in this field is not a phase at the end. It is the thing that makes shipping possible at all.
A proper set of evaluations does four jobs. It tells you whether you can ship. It catches quality drops when somebody changes a prompt, swaps a model, or rebuilds an index. It lets you compare two options without arguing. And it gives a stakeholder evidence rather than impressions, which is the difference between a decision and a debate.
Evaluation-driven development means writing the test before or alongside the feature, and letting the test rather than anyone's opinion decide when it is finished. I have built test sets and automated grading pipelines with routine expert review on clinical AI work, and it is the single most useful habit I can pass on. The rest of this lesson is how to do it rigorously.
Those sections are part of the paid course.
Answer in your head first, then reveal. Getting it wrong is the part that makes it stick.
Why is "we tried it and the answers looked good" not evidence?
The rest of this lesson
- The four axes of evaluation
- Building a test set that is actually worth having
- The four numbers that diagnose a retrieval system
- Using a model to grade other models
- Precision and recall, with actual numbers
- Telling a real improvement from noise
- Public benchmarks, and why they wear out
- After launch: watching it in the wild
- One worked example: proving a legal agent is safe
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