User Feedback Loops for LLM Applications
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AI FeedbackLLM EvaluationProduct AnalyticsAI Quality
LLM quality changes over time as prompts, models, users, and data change. User feedback helps teams find what to improve.
Feedback signals
Collect:
- thumbs up/down
- regeneration clicks
- user edits
- issue reports
- abandoned outputs
- support complaints
Turn feedback into data
Review bad outputs and add representative examples to evaluation sets.
Avoid noisy metrics
One rating is not enough. Combine explicit feedback with behavioral signals.
Final thoughts
Feedback loops turn user experience into model improvement data. They are essential for mature AI products.