Human-AI Collaboration

Feedback Loops

Continuous learning mechanisms where user corrections and preferences improve AI performance, creating experiences that evolve with usage.

What is Feedback Loops?

Feedback Loops is an AI design pattern where systems continuously learn from user corrections and preferences to improve performance over time. Instead of making the same mistakes repeatedly, the AI captures user feedback, adapts its behavior, and creates increasingly personalized experiences. It's perfect for recommendation systems, content moderation tools, virtual assistants, or any AI that interacts frequently with the same users. Examples include Spotify learning your music taste from skips and likes, Gmail's spam filter improving from your corrections, or smart home devices adapting to your daily routines and preferences.

Example: Claude Code Feedback

Claude Code collecting and learning from user feedback on code suggestions

Allows developers to rate and provide feedback on code suggestions and outputs, helping Claude learn user preferences and improve future suggestions.

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