Feature Request: Let ChatGPT Analyse and Submit Product Suggestions Directly
I recently had a product suggestion while talking with ChatGPT.
What happened next exposed what I think is a larger missed opportunity.
I explained the idea to ChatGPT. The assistant understood the problem, challenged parts of it, helped refine the concept, identified possible benefits and limitations, and turned my rough thought into a much stronger product proposal.
Then I asked:
Why can’t you submit this idea to OpenAI for consideration?
The answer was essentially that ChatGPT has no direct internal channel to do that.
That feels like a strange gap in the product.
The assistant already understands the suggestion, the context that created it, the user problem behind it and the possible implementation considerably better than a generic feedback form ever could.
Yet the user still has to leave the conversation, find the correct feedback channel and manually recreate everything.
The idea
I would like ChatGPT itself to become a first-stage product feedback and feature-request system.
A user could simply say:
“Analyse this as a product suggestion.”
or:
“I think this should be sent to the product team.”
ChatGPT would then help turn the idea into structured, useful product feedback.
Importantly, I am not suggesting that every comment should automatically be sent to OpenAI.
The user should explicitly choose to submit it.
How it could work
1. The user identifies a problem or idea naturally
The suggestion may appear during an ordinary conversation.
For example:
“I wish ChatGPT had separate Personal and Work contexts.”
Instead of requiring the user to find a feedback page, the assistant could recognise that this may be a product suggestion and offer to analyse it.
2. ChatGPT performs first-stage analysis
Before anything is submitted, the assistant could examine:
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What problem is the user actually experiencing?
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Is this already solved by an existing feature?
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Is the proposed solution different from the underlying need?
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What are the likely benefits?
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What are the disadvantages or implementation risks?
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Could the suggestion affect privacy, security or usability?
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Is this likely to apply only to this user, or could it represent a broader need?
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Are there similar existing requests?
This is important because the goal should not be to send more feedback.
The goal should be to send better feedback.
3. ChatGPT structures the proposal
The system could generate something like:
Problem
What is creating friction for the user?
Current behaviour
What happens today?
Use case
What was the user trying to accomplish?
Proposed improvement
What could change?
Expected benefit
Why might this improve the product?
Trade-offs
What problems could the change create?
Related requests
Are there already similar ideas?
4. The user reviews exactly what will be submitted
This part is important.
Nothing should be submitted automatically.
ChatGPT should show the user the exact feedback package and allow them to:
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edit it;
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approve it;
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cancel it;
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optionally include additional context.
The whole conversation should not automatically be sent.
Only the approved product-feedback summary should be submitted unless the user explicitly chooses otherwise.
5. Similar requests could be grouped together
This could be particularly valuable for OpenAI.
If 5,000 users independently encounter the same problem, OpenAI should ideally not receive 5,000 unrelated feature requests that somebody has to manually read and connect.
ChatGPT could identify that they represent the same underlying problem.
The system might say:
“A similar proposal already exists. Would you like to add your use case and support this request?”
That gives OpenAI something much more useful than simple upvotes.
It provides evidence showing how different users encounter the same problem in real-world situations.
Why I think this could be valuable to OpenAI
ChatGPT users are effectively conducting an enormous continuous usability test.
Every day, people use ChatGPT for business, education, coding, personal organisation, research, creativity and thousands of workflows that an internal product team could never fully reproduce.
During those interactions users constantly discover friction.
Most of that information is probably lost.
A user thinks:
“It would be better if ChatGPT did this.”
But submitting feedback takes effort, so they simply continue with what they were doing.
That means potentially valuable product information disappears.
If users could instead say:
“Analyse this as feedback.”
the barrier becomes almost zero.
ChatGPT is particularly well suited to this because it can help distinguish between:
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a user simply disliking something;
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an actual usability problem;
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a misunderstanding of an existing feature;
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an edge case;
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and a potentially valuable new product capability.
Human review should still make the decision
I am not suggesting that the AI should decide which features OpenAI builds.
The AI would be an intake and analysis layer, not the product manager.
A possible flow could be:
User observation
→ ChatGPT analysis
→ Structured proposal
→ Duplicate / pattern detection
→ User approval
→ Product feedback system
→ Human review where appropriate
This could reduce noise while increasing the amount of useful feedback that reaches product teams.
Feedback status would make users much more willing to contribute
It would also be helpful if users could see a basic status for submitted ideas.
For example:
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Received
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Grouped with similar requests
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Under review
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Not currently planned
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Implemented
This does not require OpenAI to reveal confidential roadmaps or promise that a feature will be developed.
Even something as simple as:
“Your suggestion has been grouped with 2,700 similar reports.”
would show users that their contribution was useful.
Optional contributor recognition
There is also an opportunity to encourage thoughtful contributions.
I would not reward users based on how many suggestions they submit, because that would obviously create spam.
But if a contribution is later determined to have been unusually useful, OpenAI could optionally recognise it through things such as:
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ChatGPT usage credits;
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temporary access to higher usage limits;
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beta or early-access programmes;
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invitations to user research;
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contributor recognition;
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other small product benefits.
The objective would not be to turn product suggestions into a bounty programme.
It would simply create a positive feedback loop where people who genuinely help improve the product are encouraged to keep contributing.
The larger opportunity
The interesting part to me is that ChatGPT already has almost everything needed to do this.
It can understand the user’s original problem.
It can ask questions.
It can challenge assumptions.
It can compare possible solutions.
It can turn an incomplete thought into a structured proposal.
What is missing is the final bridge between:
“This seems like a useful idea.”
and
“This has now reached the appropriate product-feedback system.”
I think that bridge could turn ordinary conversations into a valuable and continuously renewable source of product intelligence for OpenAI, while also giving users a much easier way to contribute to improving the product they use every day.