Free Lesson
Ship an AI Feature You Can Trust
45 min
Oct 14, 2026 12:00 PM
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What you'll learn
How to turn a vague complaint into a labeled failure case
Users say the output feels off. Turn that into a specific case you can test against.
How to build a 50-example evaluation set in one afternoon
Fifty examples is enough to show where a system breaks. No labeling infrastructure needed.
How to tell a real improvement from noise
Two versions score 82% and 85%. Know when that gap means something and when it does not.
Why this topic matters
Most teams ship an AI feature on a gut feeling and find out from users what is broken. The gap between passing internal testing and failing for real users is where launches go wrong, and it is expensive to discover late. This session covers how to build a small evaluation set that tells you where your system fails and how often, before it reaches anyone.
