The Journey of Using AI to Help Me Code But Is It Production-Ready?
Using AI for coding has quickly become more than just a prototype. It is now part of the toolkit for developers. I still remember when I first came across AI-generated code.
It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical.
Something that began in jest, as an absurd test, became the realization that with proper use of AI, it genuinely could change the way we coded.
Like most developers, I had a pretty standard process when dealing with a new problem. It was to gather the requirements, outline a plan, get down to writing the code, and then debug when necessary.
Introducing AI into this workflow felt odd at first. It wasn’t just faster writing of code; it was how AI reshaped how I thought about coding. It wasn’t fill-in-the-blank; it was suggesting code structures that I may not have thought of pointing out possible inefficiencies helping see different approaches to the same problem.
For example, I was working on a backend project using. NET. The task was to optimize a database query, which was pulling in a lot of data.
Normally, I would dive into the query, break it up, and then refactor it until it was as efficient as possible. And so this all changed the day I started writing the query out. GitHub Copilot gave me options & much more efficient ways to do pagination and filtering that I hadn’t even thought of.
And here’s the crazy thing these weren’t just suggesting something completely random; they were really good improvements.
AI saved me time but taught me optimization techniques in real-time.
The few first attempts at working on AI tools proved that I still needed to understand the logic of codes that were being suggested to me.
There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope.
AI can help you code faster but isn’t a substitute for knowing why you’re writing that code. And that is where developers can get it wrong — too heavy on AI, not enough actual due diligence.
A perfect case in point is that I was trying to work out how to integrate Kafka with a minimal API. I knew that this kind of consumption of messages asynchronously could be optimized. Of course, it’s still not for you to put it into the correct architecture.
This time, I feel I need to share it with everyone & AI can make your life more efficient.
You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context.
The beauty of coding with AI is that it does not focus solely on the small things — those rote, boilerplate tasks. Over time, I realized how AI was guiding me through other deep design decisions like
How do I structure an application?
When should I scale?
Almost like having a junior developer who’s good at the basics but instead of just taking the AI’s output at face value, you could guide it, mentor it even, and adapt its suggestions to your needs.
That’s where AI shines, the tool for exploration and learning. It’s not like “Here’s how you write a for loop,” but more “Have you considered this alternative approach?”
Whether it’s optimizing that PostgreSQL query, implementing bulk copy functionality with NPGSQL, or tweaking connection pooling in EF Core, AI gives you options you might not have otherwise thought of on your own. It tests your assumptions and pushes you to think outside of the box.
AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application.
It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production.
It reminded me that AI tools are only as good as the data that trains them. This is still more of a human intuition and experience play in many complex scenarios.
This fusion of the strengths of both AI and human talents will define the future of coding. Applying AI the right way involves gaining the limitations of its usage and playing to its strengths.
I now see AI as a partner in coding and not as a person writing code for me, but as one that helps me code smarter. It can help me get ideas, clean up the code structure, and carry out repetitive tasks, but I still make the final calls.
To developers who haven’t yet embraced AI, I understand that. It is a natural fear of the tools that seem to take over part of our job.
Nevertheless, I always advise beginners to start small. Let AI help with unit tests for instance, or even refactoring suggestions. As you grow more comfortable, start using them in larger contexts — architecture decisions, system optimizations, or debugging.
It is very important to treat AI as an evolving tool. Like how we learn new frameworks or languages, AI systems such as Codex and GitHub Copilot improve every day. The more you use them, the better they get at understanding your coding style and preferences along with how you think about problems.
The magic of such a system is in how it evolves with you by giving progressively more valuable insights the more you work with it. What is unmagical about this is that it doesn’t do the work for you.
It’s like learning to code all over again, but you have an assistant. You wouldn’t let someone else write all your codebase, but you would listen if they offered some brilliant new way to approach a problem. That’s how I use AI now as a crutch, but as a way to broaden my horizons and improve my craft.
Now, with maturing AI, I expect the future of coding to lie not in how machines are going to replace us but in helping machines become some kind of tool that makes us better developers.
If you do AI right, it is not only about making you more productive is going to make you a better coder, open up new possibilities, and push you to think deeper, solve problems faster, and deliver more robust solutions. And, in the end, that is what code is all about.
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