2 posts tagged with "distribution-shift"

Your AI feature launched. It's working well. Users are adopting it. Satisfaction scores are up. You go back and run the original eval suite—still green. Six months later, something is quietly wrong, but your dashboards don't show it yet.

This is the co-evolution trap. The moment your AI feature is deployed, it starts changing the people using it. They adapt their workflows, their phrasing, their expectations. That adaptation makes the distribution of inputs your feature actually processes diverge from the distribution you measured at launch. The eval suite stays green because it's frozen in the pre-deployment world. The real-world performance drifts in ways the suite never captures.

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.