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Building AI Test Datasets

Create a versioned test set with clear labels, edge cases and a rubric for an AI drafting task.

3 reading lessons · about 30 min ·written by MTT

The idea

A test set should reflect the inputs and failures the workflow actually encounters. Include ordinary cases, rare but costly failures, missing information and unsupported requests. Record where samples came from and whether you may use them. A convenient set of easy prompts can give a misleading quality score.

Worked example

A support-drafting evaluation contains clear questions, vague questions and refund requests the assistant cannot authorize. If all examples are simple delivery questions, the measured success rate says little about how it handles unsupported actions or missing order details.

Try it

Write eight fictional inputs for one narrow drafting task. Label the situation each represents and include two costly failure cases. Explain which real-world input types remain missing and why your sample cannot support a claim of universal reliability.

Lesson 1 of 3 · About 10 min

Sample the real task

Check your understanding

Course quiz

Finish the course to unlock the quiz

Complete all 3 lessons and 5 questions open up here. You have 3 to go.

What you will learn

  • Sample normal and difficult cases
  • Resolve ambiguous labels
  • Keep evaluation separate from development

Before you start

Prerequisites
Basic AI workflow familiarity. All sample inputs should be fictional or authorized.
Cost
Free introductory reading lessons, exercises and quiz. Optional third-party tools, hosting or AI subscriptions may cost money.

Original introductory lessons and assessment by Master the Trick. Estimated times include the suggested exercises.