Feature Request: Conversation-Native Product Feedback With User-Approved AI Submission
ChatGPT users routinely discover product problems, edge cases, and possible improvements while actually using the system. Today, much of that information disappears unless the user stops what they are doing, finds the appropriate feedback channel, reconstructs the discussion from memory, and submits it manually.
ChatGPT itself is in a unique position to help.
When a conversation produces what the model reasonably believes is a useful product or system-design idea, ChatGPT should be able to say something like:
“That may be useful product feedback. Would you like me to prepare it for submission?”
If the user agrees, ChatGPT would create a concise structured report containing:
- The problem that was observed
- The relevant conversational context
- The proposed improvement
- Why the change might help
- Foreseeable drawbacks or failure modes
- Any uncertainty in the model’s interpretation
Nothing should be transmitted without the user seeing and approving the final report.
After approval, the feedback system could use AI to classify, deduplicate, aggregate, and route submissions before human review. If thousands of users independently encounter variations of the same problem, engineers would receive one meaningful signal rather than thousands of disconnected complaints.
This would turn ordinary conversation into a much richer source of product feedback without allowing ChatGPT to autonomously modify itself or decide which changes should be implemented.
The division of responsibility would remain clear:
AI identifies and structures potentially useful feedback.
The user decides whether it leaves the conversation.
Automated systems organize and aggregate it.
Human engineers evaluate, test, and decide whether anything is implemented.
The major advantage is that ChatGPT can understand not only what the user disliked, but often why the experience failed and what solution emerged through discussion.
Traditional software can record that a user clicked a button or submitted a complaint. Conversational AI can potentially understand the reasoning that led to the complaint.
Example:
During a conversation about an apparent false-positive safety interruption, the user and ChatGPT reasoned through a potential architectural improvement. ChatGPT recognized that the resulting idea could have broader product value, but currently has no mechanism to offer to package and submit that insight for review.
That missing capability is itself what led to this feature request.