AI User Feedback Collection Template

Industry benchmarks vary by task type. Content generation: 70-80% is typical, 85%+ is strong. Search and retrieval: 75-85% is typical, 90%+ is strong. Classification: 85-95% is typical. If your satisfaction rate is below 65%, investigate whether the AI feature is solving the right problem, not just whether the model is accurate enough.

Never ask for feedback on every interaction. Start with 100% for launch monitoring, then drop to 10-20% sampling once you have a baseline. Time your feedback requests after the user has had a chance to evaluate the output (not immediately). Make the feedback mechanism single-click (thumbs up/down), with optional depth (categories, comments) only when the user signals dissatisfaction.

Not automatically. User corrections are valuable but noisy. Some corrections are wrong, some reflect personal preferences rather than quality issues, and some contain PII. Build a review layer: corrections that match patterns from multiple users are high-confidence training signals. Individual corrections should be reviewed before inclusion. Always get [user consent](/glossary/prioritization) for using their corrections in model improvement.

Contradictory feedback usually means the AI is in a subjective domain where different users have different expectations. Segment feedback by user type or use case. If power users love verbose responses and new users prefer brevity, that is not a model problem. It is a personalization opportunity. Track whether contradictions cluster by user segment.

Immediately for any feedback categorized as Harmful/Unsafe. Set up automated alerts for this category. Also escalate when you see a cluster of "Incorrect" feedback on a sensitive topic (medical, legal, financial) even if no individual report triggers the safety threshold. The [AI Ethics Scanner](/tools) can help identify which topics require heightened monitoring.