Feature Proposal: Native Categorization of ChatGPT Query History
Overview
This proposal suggests enabling ChatGPT to access and intelligently categorize a user’s own past queries
into themed groups or projects within the ChatGPT interface. This functionality would help users revisit
relevant discussions and avoid redundant queries without requiring external integrations.
Problem
As users interact with ChatGPT across a wide range of subjects (e.g., tech builds, health, product questions),
their questions become scattered and difficult to track. This results in inefficiencies, as users often re-ask
questions or lose valuable research in past sessions.
Proposed Solution
Enable ChatGPT to review a user’s historical prompts and responses and suggest thematic categories such
as ‘Tech Builds,’ ‘Product Listings,’ or ‘Health Questions.’ This organization would be available through a dashboard where users can manage, merge, or tag entries. All processing would remain within the ChatGPT platform.
Design Principles
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Opt-in user control and transparency
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No access to external tools or accounts ( just yet)
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Data remains private and processed within ChatGPT
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Read-only access to existing session history
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Lightweight categorization logic built on existing memory systems
Business Value: Cost Reduction and Efficiency Gains for OpenAI
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Reduces repeated questions by helping users find previous answers
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Lowers token usage per session, improving backend efficiency
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Enhances user satisfaction and platform loyalty
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Supports better memory contextualization by organizing user contentFeature Proposal: Native Categorization of ChatGPT Query History
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Positions ChatGPT as a personal knowledge base rather than a one-time assistant
Conclusion
Implementing this feature could transform ChatGPT into a true personal knowledge assistant. It supports
smarter conversations, boosts long-term value for users, and creates operational efficiencies for OpenAI. It’s
a low-risk, high-impact enhancement to the platform.