Module 2 of 6Lesson 2 of 2~16 min
Define measurable success criteria
How to turn the goal of an AI feature into success criteria the team can measure and agree on. Without this work, everyone judges quality their own way and launch discussions go in circles; with it, you know which errors are tolerable and which ones should stop a release.
Lesson objective
By the end of this lesson, you will be able to turn an AI feature's goal into specific, measurable, achievable and relevant success criteria, covering several dimensions and ranked by error severity.
Questions this lesson answers
- How do you define measurable success criteria for an AI feature?
- Why isn't one overall score enough to evaluate an AI feature?
Topics covered
- success criteria
- error severity
- quality dimensions
- AI feature evaluation
In the glossary
Where it fits
Why evaluate, and what to measure
What proves that an AI feature works, and against which criteria?
Lessons in this module
- Why vibes are not enough
- Define measurable success criteria (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
- Turn an AI feature's goal into specific, measurable, achievable and relevant success criteria, taking error severity into account.
Related courses
- AI & ProductAdvancedRegular practice: you already use the tools or work on this topic.
Build an AI assistant for your product
3 hr
- AI & ProductAdvancedRegular practice: you already use the tools or work on this topic.
Design a RAG architecture that fits your product
3 hr 30 min
- AI & ProductExpertAdvanced level: for deeper mastery and specialization.
Ship and monitor an AI feature in production
3 hr