Module
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.

Take this lesson: your first course is freeSee the full syllabus

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.

Topics covered

  • success criteria
  • error severity
  • quality dimensions
  • AI feature evaluation

Where it fits

Why evaluate, and what to measure

What proves that an AI feature works, and against which criteria?

Lessons in this module

  1. Why vibes are not enough
  2. Define measurable success criteria (this lesson)

What you will learn in the course

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.
  • 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.
  • Choose, for each criterion, a grading method (code, human, LLM judge) and justify the trade-off between cost, speed and reliability.
  • Design an LLM judge (rubric, format, different model), identify its biases and calibrate it against human grades.
  • Choose and interpret the right metrics for a classification and for a RAG system, and infer which stage to fix.
  • Define the regression rule, how offline and online evaluations fit together, and release thresholds set before seeing results.

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