Feedback

Updated May 25, 2026

Feedback loops is an AI interface design pattern that collects user feedback on AI-generated content through simple interactions like thumbs up/down buttons, rating systems, or detailed feedback forms. This UX pattern enables reinforcement learning from human feedback (RLHF) by allowing users to indicate whether responses were helpful, accurate, or appropriate. The feedback is used to improve the AI model over time, making responses more aligned with user preferences. This pattern is essential for all AI applications where continuous improvement is important, as user feedback directly informs model training and refinement. It creates a collaborative relationship between users and AI, where user input helps the system learn and improve.

Use Case

Essential for all AI applications where collecting user feedback enables continuous improvement and better alignment with user needs and preferences.

Examples in Wild

ChatGPTClaudeGoogle BardPerplexity

Use this pattern in your project

Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.

Generate a production-ready implementation of the "Feedback" AI interface design pattern.

Pattern Description:

See full prompt

Copy Prompt