FileGPT

At the time, researchers and students struggled with overwhelming volumes of academic papers, lecture recordings, and transcripts. Reading and note-taking consumed hours every week, leaving little time for higher-value tasks like analysis and synthesis.

Students often spend hours just trying to extract key points from dense documents.

A survey of graduate students showed that 65% spent 5+ hours per week skimming papers for relevance, and 47% admitted they rarely finished full articles, relying only on abstracts or notes. Early testers also reported that generic AI chat tools were not sufficient, since they couldn't process PDFs, transcripts, or lecture videos directly.

All signals pointed to the same need: a smarter assistant that could summarize diverse content formats and surface insights in minutes instead of hours.

Designing FileGPT taught me how AI reshapes design thinking and the realities of product development beyond the classroom.

Working on FileGPT when UX Pilot had just gained public attention revealed a new set of challenges for design. Unlike traditional UX flows, generative AI created unpredictable outputs, requiring me to prioritize credibility, transparency, and adaptability as part of the design.

This experience also highlighted the gap between academic UX training and real-world product development. In school, I was taught to conduct comprehensive, rigorous research, but I quickly realized that such methods were too time-intensive for a startup environment. Instead, I learned to adapt by running lightweight, rapid validations that still informed impactful decisions.

Working closely with developers showed me that design cannot be separated from technical feasibility. Many user flows and trust features, such as source traceability, depended on implementation details. This collaboration gave me a deeper appreciation for how design and engineering shape each other.