I've been learning to code for about five months without using AI. I've worked through books and syntax courses, built several projects, and feel that I have a solid understanding of how computers and programming work compared with many other beginners.
Recently, I wanted to experiment with a compression algorithm inspired by what I've learned about how images are stored. I designed the basic idea myself and implemented it in Python, using OpenCV and an image-background-removal API. I used AI to generate much of the code because I wanted to test the concept quickly rather than spend a lot of time reading documentation.
I didn't simply ask it to build the entire project. I gave it specific instructions, review the generated code line by line, document it, and make sure I understand what it does. When something breaks, I can usually diagnose the problem myself and then use AI to help apply a fix.
Still, I feel a little uneasy because this is the first project where I've relied on AI so heavily. I mainly used it because I was impatient and thought learning the required libraries on the fly would be faster. Is this a reasonable way to use AI, or am I developing bad habits? How do experienced developers balance AI assistance with actually learning and retaining programming skills?
5 Answers
There isn't a single correct level of AI assistance. The unhealthy extreme is letting it choose the design, write the core logic, and fix errors until something happens to work. What you described is better because the main idea came from you and you are checking the implementation. Just keep setting aside time for small exercises and projects where you write the core code without assistance, so your independent problem-solving ability continues to grow.
Using AI is fine as long as it supplements learning instead of replacing it. It can save time on repetitive tasks, awkward syntax, or things like regex, but you should still understand and verify the result. If you can explain the code, test it, debug it, and modify it without blindly prompting until something works, you're using it much more responsibly than someone outsourcing the entire project.
One risk is that AI infers your intent instead of translating your design exactly. It may quietly alter the algorithm and produce something that works but is not actually the idea you wanted to test. Writing a small version yourself can reveal design mistakes and teach you through the debugging process. AI is safer for boilerplate, repetitive tests, or clearly specified transformations where you can easily audit the output.
The point about AI inferring intent is helpful. I hadn't fully considered that it could subtly change the algorithm while still producing plausible code.
Your approach sounds reasonable for an experiment, especially since you came up with the algorithm and are reviewing the generated code. Once you decide that a topic is worth learning deeply, though, slow down and implement important pieces yourself—or rewrite the AI version after studying it. That lets you move quickly during exploration without giving up the chance to build lasting skills.
That is close to how I want to use it going forward: quick prototypes first, followed by careful analysis or a rewrite when the project becomes something I want to understand deeply.
Think of AI as a productivity tool, not as a replacement for your programming knowledge. The concern is less about whether it generated the code and more about whether you can reason about the problem yourself. If you stopped using AI tomorrow, could you still design a solution, read the documentation, and debug the important parts? Keep practicing those skills so the tool doesn't become a crutch.
A calculator analogy only goes so far: you still need to understand the problem and enter the right formula. AI can remove more of the reasoning, so it's especially important to work through some problems unaided.
That distinction matters most with complicated code. AI can make basic syntax mistakes or produce something subtly different from what you intended, so its output still needs to be checked against documentation and tests.