Overview
How might we design feedback so people can trust and act on AI output?
When this pattern fits
- Essential for all AI applications where collecting user feedback enables continuous improvement and better alignment with user needs and preferences.
When to skip or lighten it
- Internal one-off tools with no model or ranking pipeline to consume the signal.
- High-stakes decisions where a thumb is too blunt and structured review is required.
- Every micro-token during streaming; wait until the answer settles.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Feedback that vanishes with no confirmation or effect the user can observe.
Only thumbs down with no reason codes, leaving teams unable to triage.
Forcing a rating modal after every message.
Using feedback solely for marketing metrics while never feeding training or retrieval.
How products use it
| Product | Implementation |
|---|---|
| ChatGPT | Thumbs and optional free-text feedback on assistant messages. |
| Claude | Message-level feedback controls on responses. |
| Gemini | Positive/negative feedback and report flows on answers. |
| Perplexity | Feedback on answer quality and wrong-source issues. |
Real-world examples
How shipped products implement feedback, from our teardown guides.
ChatGPTChatGPT output UX: in-thread replies & refinement
Output, artifacts & refinement
ClaudeClaude output UX: replies, retry & feedback
Output, artifacts & refinement
GeminiGemini output UX: guides, tables & Workspace handoffs
Output, artifacts & refinement
PerplexityPerplexity citations UX: source chips & trust design
Trust, privacy & settings
PerplexityPerplexity output UX: tabs, follow-ups & export
Output, artifacts & refinement
Implementation
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 Definition:Frequently asked questions
What are feedback loops in AI product UX?
Feedback loops are UI controls that let users rate or flag AI outputs so the product can improve ranking, fine-tuning, or human review, and so users feel heard when something is wrong.
Should every AI message have thumbs up/down?
Usually yes for chat products that improve models or retrieval from feedback, as long as controls stay quiet and optional. Skip noisy forced ratings on high-frequency ephemeral UIs.
What should happen after a thumbs down?
Offer optional reason chips (wrong, unsafe, not useful) confirm receipt, and ideally a path to regenerate or refine. Silent collection without acknowledgment lowers trust.