One of the biggest challenges I’ve noticed during my internship at Carvago is learning how to work through uncertainty. I’m currently working on projects involving artificial intelligence (AI), and if there’s one thing I’ve realized, it’s that the field is constantly changing. New tools, methods, and research come out all the time, which can make it hard to always know the “right” way to approach something. Even when I’m assigned a task, there are often parts where I’m not 100% sure what the best next step is, or how to apply what I know to this specific problem.
The good thing is, I’ve learned that this kind of uncertainty is normal in tech. Unlike a school assignment where the directions are very clear and there’s usually one correct answer, the problems I’m working on now are more open ended. For example, if I’m building or refining an AI agent, I might be given a goal like getting it to classify or rate something accurately, but the path to get there isn’t always obvious. There are choices to make about what model to use, what data to include, how to test it, and so on.
To navigate this uncertainty, I’ve found that a mix of research, communication, and trial-and-error works best. When I don’t fully understand something or run into a roadblock, the first thing I usually do is try to research it on my own. I look up articles, tutorials, documentation, etc. to see how other people have approached similar problems. The internet has been an incredibly helpful tool and often gives me a good place to start.
When I’ve tried a few things and still feel stuck, I’ll reach out to my supervisor on Slack. The company uses Slack for all internal communication, so it’s easy to ask quick questions or check in about how I’m doing. I’ve learned that it’s okay not to know everything and that it’s better to ask a thoughtful question than stay confused. My boss is really understanding and often helps point me in the right direction, or explains how the team usually handles similar problems.
One thing I’ve also noticed is that I’m slowly getting more comfortable making decisions even when the instructions aren’t crystal clear. If I understand the overall goal and timeline, I’ve started trusting myself to make reasonable choices, test them, and adjust based on the results. I think this is a really important skill to build, especially in a field like AI where there isn’t always a fixed roadmap.
Outside of work, I ran into a different kind of uncertainty this week which was deciding where to eat. My friends and I decided to go to a mall here in Prague just to walk around, shop a little, and take a break from work. We went clothes shopping and browsed a few stores, but when it came time to eat, we couldn’t agree on a place. Everyone wanted something different, and we weren’t familiar with most of the options in the food court.
After a while of going back and forth, we noticed a fast food style Indian spot, Bombay Express. We hadn’t heard of it before, but it looked decent, and the prices were fair, so we decided to give it a shot. Surprisingly, the food was actually pretty good, especially for mall food. We got large portions for a good price, and it turned out to be a fun and unexpected win. It reminded me that sometimes, when there’s no clear choice, the best thing you can do is try something, and it might end up better than expected.
That’s sort of how I’m approaching this internship too. There’s a lot I still don’t know, and sometimes I hesitate because I want to be sure I’m doing the “right” thing. But more and more, I’m learning that taking action, learning from it, and adjusting as I go is often more valuable than waiting for the perfect plan.
Working in a constantly evolving field like AI means I’ll always be learning. The tools I’m using today might not be the same ones I use a year from now. So rather than trying to be perfect, I’m focusing on being curious and knowing how to find information and ask for help when I need it.
Overall, this week reminded me that uncertainty is part of both work and everyday life. Whether it’s choosing the right model for an AI task or picking where to eat at the mall, sometimes the best approach is to trust yourself and learn from what happens.