When I first started exploring artificial intelligence, I didn’t think of it as some grand career path. I was simply just curious. Like a lot of people, I opened a tool, typed a few prompts, and watched words and images appear on the screen. At first, it felt like magic to me. But very quickly I realized something important which is that the tool itself wasn’t intelligence. The intelligence came from how you used it. That realization became the starting point of my journey. 

In the early days, my biggest advantage was curiosity. I treated AI like a practice partner rather than a shortcut. As a computer science student at the University of Washington Tacoma, I was already spending long hours learning programming and solving problems, but AI gave me a way to push further. I could ask it to explain data structures and algorithms, challenge my thinking, or simulate interview style questions. Instead of replacing my work, it amplified the work I was already doing. Over time, it became one of the tools that helped me grow as a developer.  

But the opportunity came with challenges that I didn’t fully understand at first. AI is incredibly powerful, but it is also incredibly confident when it is wrong. Early on I learned that the tool will follow your direction, even when your direction is flawed. If you ask vague questions, you get vague answers. If you don’t check the results, you can easily build on top of mistakes. That forced me to develop a new kind of discipline which is to verify outputs, question assumptions, and remember the responsibility for the final work always belongs to the person utilizing the tool. 

Another challenge was learning not to rely on AI too early. When you're starting out, it can be tempting to let the tool do the thinking for you. I made that mistake a few times. Eventually I realized that AI works best when it supports my thinking, not when it replaces it. I started solving problems on my own first, especially when practicing coding or working through algorithms. Only after I had struggled with the problem would I bring AI into the process to compare approaches or explain concepts I didn’t fully understand. 

If I could go back and give my younger self advice at the beginning of this journey, the first tip would be to treat AI like a mentor, not an answer sheet. Ask it to explain ideas, critique your work, and challenge your reasoning. The goal is not to get the answer faster, the goal is to understand the problem deeper. 

The second tip is something I wish more beginners heard which is that your prompts reflect your thinking. Early on, I thought prompt engineering was about finding clever phrases. Over time I realized that it’s really about clarity of thought. The more specific and intentional your questions are, the better the results become. Good prompts come from understanding what you’re trying to achieve. 

Looking back, learning AI wasn’t just about learning a technology. It was about learning a new way of thinking. The people who will benefit most from AI are not the ones who chase every new tool, but the ones who develop judgment, curiosity, and patience. If you’re just starting your AI journey right now, remember that the real power isn’t in the model it’s in the person guiding it.