Build Your Own “Custom GPT”: How to Put It to Work Smarter for Your Goals

Why Read This from an HCI Perspective

This article helps you see AI not just as a feature, but as a matter of user experience. In particular, the perspective of designing when users should trust the AI, when they should stop, and where they should intervene is crucial for HCI and UX practitioners. Just as important as making the model smarter is ensuring the interface is clear—because only then can the service be used safely and effectively in real settings.

CIT's Commentary

It’s easy to think of AI only as a tool for automating work, but in reality it’s an interaction problem where people and systems move together. The custom GPT flow described here is less about “generating better outputs” and more about “producing consistent results” and “giving users a way to check and correct them.” Especially in Korea’s service environment, where fast execution and high trust are both required, not only the quality of default responses but also intervention paths such as status indicators, undo options, and error visibility become more important. From this viewpoint, LLMs should be designed not as replacements for products, but as assistants that people can control.

Questions to Consider While Reading

• Q.When attaching a custom GPT to work, how can you show users the points at which they can trust the results?
• Q.In jobs where consistent output matters, how should you balance automation convenience with the user’s ability to intervene?
• Q.In the context of Korean services like Naver, Kakao, and startups, what kind of interface design is needed that differs from global examples?