I’ve been using AI tools across several projects, and while learning to build an FAPI, I saw how AI speeds up prototyping—but real engineering experience was crucial to catch edge cases, design robust architecture, ensure performance and security, integrate systems smoothly, and keep code clean, maintainable, and scalable.
Before diving into my experience, let me set the stage. AI tools have changed the way we write code: they can finish functions, build modules from simple descriptions, and even suggest design ideas as you type. As these tools become more common, I’ve been experimenting with them—pushing AI to speed up development. In this post, I’ll share how “AI vibe” coding fits into my work, the benefits I’ve seen, and the problems I’ve learned to watch for.
As a software engineer and a tools/product developer, I always have the itch to build something and see isolated software systems work together. The working software kicks the kind of dopamine that makes you want to try new things which is good for learning and upskilling—but it can come at the cost of burnout. Well, that remains another topic. What I really want to share in this post is my experience with AI vibe coding for engineering solutions and building tools that keep my itch and solution-finding craving satisfied.
Over time, I’ve become proficient with prompt engineering strategies and my modus operandi to build software tools. Still, I understand why shipping software fast is a double-edged sword. It certainly helps you launch quickly, but on the other hand, it can make the code messy and hard to maintain if you let the AI tool do all the thinking based solely on the immediate task context. As a software engineer, it’s your responsibility to think ahead and beyond the current implementation—ensuring that the software remains extensible, maintainable, scalable, and free of crippling technical debt.
If you don’t take the time to review and address potential issues and bottlenecks early, an AI vibe coding solution can create problems that take far longer to fix than writing the code yourself in the first place. AI tools and agents are fantastic for productivity and can be lifesavers in many aspects, but they have their place in the software development lifecycle—they cannot replace it entirely. There’s a thin line between using an AI tool to help solve a technical problem and letting the AI tool solve the problem for you. Staying in control of your code is what matters most for shipping consistently.
Over the past year, AI-driven coding tools have gained traction around the world. The global market for AI code tools was valued at $6.7 billion in 2024 and is expected to reach $25.7 billion by 2030. In controlled studies, developers using an AI pair programmer like GitHub Copilot completed tasks about 50 percent faster than those without. In my own work, I’ve learned how to write prompts that get useful code back from these tools—treating AI as a helpful assistant rather than a replacement for my own thinking.
That said, AI can introduce gaps in your workflow. For example, while I was experimenting to build a robust plug-and-play Frontend API for an Agentic MCP chat system, I felt the pinch firsthand. The AI would generate a working stub, but I found missing edge cases, mismatched interfaces, and unclear error handling that only showed up later. In those moments, I saw how AI-generated code can create more work if you skip careful design and validation. If you rely too heavily on the AI without adding your own checks, you can end up chasing bugs that cost more time than writing the code from scratch.
That’s why I see AI as a teammate rather than a replacement for core engineering work. With my “AI vibe” approach, I let AI draft rough versions—like sample modules or API clients—then I step in to clean up, optimize, and make sure everything fits the project’s needs. By combining AI speed with careful reviews and refactoring, you get fast progress without sacrificing code quality.
AI is a powerful tool, but it can’t replace human judgment. It doesn’t know your project’s long-term goals or spot subtle design issues. By controlling how we prompt AI, checking the code it generates, and spending time on reviews, we keep our software flexible, maintainable, and scalable.
Sources:
Research and Markets. “Artificial Intelligence Code Tools – Global Strategic Business Report.” Globe Newswire, March 26, 2025.
www.globenewswire.com
Peng, Sida, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer. “The Impact of AI on Developer Productivity: Evidence from GitHub Copilot.” arXiv, February 13, 2023.
https://arxiv.org/abs/2302.06590
