Accuracy is high on paper still users complain about answers

High paper accuracy can be deeply misleading when the benchmark does not reflect what users actually care about. A model may score well on clean validation sets and still frustrate customers because the answers arrive in the wrong tone, miss practical context, or fail in edge cases that matter more in real life.

Users are not grading your system like a research paper. They are asking whether it helped them complete a task with confidence. That is why a technically correct answer can still feel wrong from the customer side.

The fix is to broaden the definition of quality. Evaluate usefulness, trust, completeness, effort saved, and recovery behavior alongside factual correctness. Once the evaluation mirrors lived usage, the gap between internal metrics and customer complaints becomes much easier to explain.