Running Evaluations as Part of the Workflow
Explore how to run evaluation sets repeatedly as part of your AI feature development workflow. Understand the importance of saving baselines, detecting regressions immediately after changes, and automating these checks to prevent unnoticed failures. This lesson equips you to maintain reliable AI features through continuous evaluation.
Everything this course has done with the evaluation set so far has run it once, to check a specific version of a prompt against a specific set of cases. A feature keeps changing after launch, a prompt gets tightened, a new capability gets added, and none of that stops the evaluation set from being exactly as useful as it was on day one, if it keeps running every time something changes instead of sitting unused after the first pass. In this lesson, we will cover:
Why running an evaluation once tells us about launch day and nothing after it
Regression versus drift, two different ways a feature gets worse
Saving a baseline and checking every change against it
Wiring the evaluation set into the workflow the way a test suite gates a deploy
How this shows up in a real product
The three ways engineers get this wrong
What running it once actually tells us
An evaluation set run before launch confirms the feature worked on the day it ...