As a Master’s student who also works part-time as a software engineer, I’ve been relying more and more on generative AI tools in my daily life. At first I treated them as a novelty, but over time they’ve become a multipurpose tool for both my academic and professional life. Still, GenAI is not a magical tool that solves everything, its limitations have become increasingly clear to me over time.
One of the earliest ways I used GenAI was for search. Instead of digging through ten different Google results, I could ask a direct question and get a straight answer. This saved time when I was researching for papers, looking up a software library, or just wanting an answer to random questions I had (as one does). However, I was cautioned against trusting the answers blindly. I quickly experienced first hand that sometimes the AI gives outdated information or confidently states something incorrect. So I do additional research, depending on how high the stakes are.
Another major use case for me is summarizing. During my studies I often have to digest long articles, papers, or lecture notes. Letting the AI condense 20 pages into a quick summary was a game changer. Of course I still have to do more in-depth reading when I need to fully understand an argument, but it gives me a head start and helps me prioritize what to focus on.
GenAI is quite good at brainstorming and drafting too. Whether for a group project in class or when sketching ideas for a feature at work, it provided prompts and perspectives I wouldn’t have come up with myself. The downside is that its creativity can be surface-level, models often just regurgitate variations of ideas that were in their training data. So if I want to come up with something truly novel, I try to think of it myself and then use GenAI for “validation”.
In terms of drafting, I’ve used it to outline essays, emails, and even software documentation for my work. It’s great for overcoming writer’s block and speeding up the initial phase. Still, if I don’t rewrite and refine the draft myself, it’s easy to see that it was generated by AI because it sounds generic.
In my work, I use GenAI mostly for boilerplate code and bug explanations. It saves me time on repetitive tasks. But in complex systems, its contextual capabilities fall short. I’ve had it produce code that looked correct but had subtle flaws, not in syntax but how it used other functions in the codebase. Also, at times it has difficulty adhering to the design philosophy and stylistic choices of larger projects.
Finally, I’ve even used GenAI for language learning (currently Dutch). It’s particularly good for practicing small conversations and checking grammar. That said, I heard from a couple Dutch friends that it sometimes uses phrases that feel unnatural to native speakers. However, for now its Dutch is definitely better than mine, so I will continue using it for learning.
In sum, I already use GenAI for a variety of tasks, and I’m sure I will continue to discover new ways it can be useful. Have you tried any of the use cases I mentioned? What was your experience? I’m curious to hear.
