LANDMARK · 7 MIN · BUILD
Building evaluation sets
After this landmark, you can build a small, representative evaluation set for an AI feature and use it to catch regressions before they ship.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
An evaluation set is a fixed collection of representative inputs with known-good (or known-acceptable) outputs, used to score a system objectively instead of by impression. Without one, ‘testing’ a prompt or model change means trying a few examples by hand and trusting the vibe, which reliably misses regressions, because a change that improves the three cases you tried can silently break ten cases you didn’t.
A good evaluation set includes the easy cases, the hard edge cases, and a few cases the system is known to get wrong, so it can show whether a change actually helped.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
What this looks like for you
Not your layer directly, but it explains the difference between AI products that stay reliable over updates and ones that seem to randomly regress; one had a real evaluation set behind changes, the other shipped by feel.
MAKE A DECISION
A team wants to improve their AI feature’s prompt to fix a specific complaint from users. What should happen before the new prompt ships?
CARRY THISFor one AI feature you maintain (or wish existed for one you use), sketch 10 test cases it should be evaluated against, including at least two known-hard edge cases.