What Quality Means for an AI Feature

Every AI feature starts life with the same quality bar: "it looks good." Someone runs ten examples in a playground, the outputs read well, and the feature ships. Then the first real complaints come in. The drafted reply promised a refund nobody approved. The summary was accurate but skipped the one detail the agent needed. The format broke the mobile app. None of these failures were tested, because "looks good" never said they mattered.

This module builds a practical quality system for AI features, and it starts here, before any eval code: turning "make it good" into a short list of criteria you can check, one feature at a time. By the end of this lesson you'll be able to write a quality spec for a feature: what good means, how each part gets measured, and which failures stop a release.

Why "it looks good" isn't a quality bar#

Traditional software has a crisp notion of correct: the function returns 42 or it doesn't. AI features produce open-ended output where many answers are acceptable, a few are subtly wrong, and some are dangerous. Reading a handful and nodding along tests none of that.

Three things make "looks good" especially misleading:

• Fluency hides errors. Model output is grammatical and confident whether or not it's right. Your brain grades the writing, not the facts.
• Quality has several independent parts. A reply can be correct and rude, polite and incomplete, complete and in the wrong format. One overall impression blurs them together, so you can't tell which one got worse after a change.
• Averages hide the failures that matter. "Users rate drafts 4.2 out of 5" can coexist with one draft in a hundred promising money you don't owe. The rare, severe failure is often the one that decides whether the feature survives.

The fix is to break quality into named criteria: specific, checkable statements about an output, each with a clear pass and fail.