Module 2 of 6Lesson 1 of 2~14 min
Why vibes are not enough
Why a successful demo and a few internal trials don't prove an AI feature is ready to ship. You get the arguments to win evaluation time from a team eager to launch, and to explain what a repeatable evaluation protects when the prompt or the model evolves.
Lesson objective
By the end of this lesson, you will be able to explain why a few tries do not prove an AI feature's quality, and to describe what a replayable evaluation adds on every change.
Questions this lesson answers
- Why isn't a successful demo enough to validate an AI feature?
- What is vibe-based evaluation of an AI feature, and why is it risky?
Topics covered
- LLM evals
- vibe check
- LLM non-determinism
- prompt regression
- testing an AI feature
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 (this lesson)
- Define measurable success criteria
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.
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