The Feedback Loop Trap: Why AI Features Degrade When Users Adapt to Them
· 10 min read
Your AI search feature launched three months ago. Early evals looked strong—your team ran 1,000 queries and saw 83% relevance. Thumbs-up rates were good. Users were engaging.
Then six weeks in, query reformulation rates started climbing. Session abandonment ticked up. A qualitative review confirmed it: users were asking different questions than they were before launch, and the model wasn't serving them as well as it used to.
Nothing changed in the model. Nothing changed in the underlying data. The product degraded because the users adapted to it.
This is the feedback loop trap. It is qualitatively different from the external concept drift most ML engineers train themselves to handle—and it is far harder to fix once it starts.
