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.
Topics covered
- LLM evals
- vibe check
- LLM non-determinism
- prompt regression
- testing an AI feature
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 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.
Related courses
- Build an AI assistant for your productAdvanced · ~3 hr
- Design a RAG architecture that fits your productAdvanced · ~3 hr 30 min
- Ship and monitor an AI feature in productionExpert · ~3 hr