Building an Evaluation Set from Real Cases
Explore how to create effective evaluation sets using real-world cases, hand-written scenarios, and corrected failures. Understand what makes cases worth including, how to start with a manageable number, and why tagging cases is essential to avoid hidden failures. This lesson equips you to monitor AI feature performance accurately before deployment.
The interface chapter finished with a contract we can write, make more likely to be followed, and enforce in code. None of that tells us whether the feature actually works on real input. This lesson builds the thing that answers that question, an evaluation set, starting with where its cases come from and what earns a case a place in it.
In this lesson, we will cover:
Where real test cases actually come from
What makes a case worth including, beyond just having a right answer
Roughly how many cases is enough to start
How to tag cases so an aggregate pass rate can’t hide a failure
How the same practice shows up in real products
The three ways engineers get this wrong
Where real test cases actually come from
Three sources cover most of what an evaluation set needs, and each one makes up for a gap the others leave.
Production data. Actual past conversations are the most ...