User feedback missing hard to improve system

Without user feedback, AI improvement becomes guesswork. The team may see outputs, logs, and metrics, but still miss the one thing that explains whether the answer was actually helpful to the person using it.

Feedback does not always have to be a rating button. It can come from escalations, corrections, repeat queries, abandonment, or other behavioral signals that show where the experience is failing.

The most useful systems make feedback easy to capture and easy to act on. Once the product learns from real user behavior, improvement gets much faster and less theoretical.