How to Use the Projects Feature in ChatGPT
Using projects in ChatGPT
HCI Today summarized the key points
- •This article explains how to use ChatGPT’s Projects feature to organize conversations, files, and instructions in one place.
- •Projects group multiple conversations and files, allowing users to continue related work from a single location.
- •Users can add the instructions they want to a project so ChatGPT responds using the same criteria.
- •This feature is especially useful for managing ongoing work, making it easier to continue without losing what you’ve done.
- •In the end, the Projects feature is a tool that makes it easier to organize and collaborate—helping you work more systematically.
This summary was generated by an AI editor based on HCI expert perspectives.
Why Read This from an HCI Perspective
This article helps you see AI not as a ‘smart feature,’ but as something users work with—an interactive partner. For HCI/UX practitioners and researchers, it suggests that interaction elements such as trust, user control and intervention, and failure recovery may matter more than raw model performance. It’s especially meaningful because it prompts you to consider how responsibilities are divided and how user workflows change once this capability is built into a product.
CIT's Commentary
AI agents and automation features may look convenient on the surface, but in practice the key is the interface that tells users when to trust and when to stop. Instead of reducing the number of buttons, if the system state becomes unclear, users end up taking on greater risk. So it’s not enough to focus only on performance improvements—you also need to design, together, where users can intervene when something fails and what signals the system uses to communicate its state. The interesting part is that solving these problems well requires sophisticated UX measurement tools and evaluation methods. Research rigor shouldn’t loosen just because you’re using LLMs; if anything, more transparent validation approaches are required. In a domestic service environment where teams add features quickly, this balance becomes even more important.
Questions to Consider While Reading
- Q.What is the minimum set of state signals that enables users to predict the AI’s next action?
- Q.When automation fails, how far should the intervention points that the system hands over to users go?
- Q.When building UX measurement tools using LLMs, what criteria are needed to preserve research rigor?
This commentary was generated by an AI editor based on HCI expert perspectives.
Please refer to the original for accurate details.
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