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Module
Module 3 of 6Lesson 2 of 2~14 min

Label, version and protect the test set

How to get a test set labeled consistently, version it and shield it from overfitting. For a PM, this is what makes results comparable from one version to the next, and it keeps you from celebrating a rising score while the feature itself has not improved.

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Lesson objective

By the end of this lesson, you will be able to have a test set labeled with a guide, check inter-annotator agreement, version it with the prompt and model, and protect it from overfitting with a holdout set.

Questions this lesson answers

  • How do you get a test set labeled consistently across annotators?
  • Why keep a holdout set when evaluating an AI feature?

Topics covered

  • data labeling
  • inter-annotator agreement
  • test set versioning
  • holdout set
  • prompt overfitting

Where it fits

Build the test set

Which cases do you test on, and how do you keep the set reliable over time?

Lessons in this module

  1. Build a representative test set
  2. Label, version and protect the test set (this lesson)

What you will learn in this module

This lesson is part of the course Evaluate an AI feature: test sets, metrics and LLM judges

  • Build a representative test set from real traffic, edge cases and adversarial cases, and justify its composition.
  • Label the test set with a guide, measure inter-annotator agreement, version it and protect it from overfitting.

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