Let me tell you about my first time using an AI coding assistant. Picture this: I enter a prompt and sit at my desk watching in slack-jawed amazement as my digital partner spins up hundreds of lines of perfectly formatted code faster than I can brew my morning coffee. “Add authentication!” POOF! “Create a data visualization!” WHOOSH! “Fix this bug!” ZING!

I felt invincible — like I’d discovered a cheat code for software development.

Oh, My Sweet Summer Child

Don’t get me wrong — that initial magic is real. Going from zero to a thousand lines of code has never been easier. But as my projects grew more complex, I found myself juggling 15 browser tabs, switching between AI chat and integrated development environment (IDE), and watching my productivity drip away with each context switch. The cognitive load was starting to feel like carrying water in a leaky bucket.

It’s worth noting here that at this stage I was just working within a web interface (picture ChatGPT in a website, and you’ve got it). Switching to an IDE-embedded chat window with access to multiple large language models (LLMs) with a drop-down made an enormous difference in both productivity and results.

Here’s the thing that took me an embarrassingly long time to remember: Writing code is just one of many pieces of software development. Something about that initial AI coding magic made me temporarily forget everything I knew about proper software development lifecycle principles. I was like a kid with a new superpower, trying to solve every problem by shooting laser beams at it.

The first breakthrough came when I stopped treating AI as just a code generator and started using it as a partner in the entire development process. I built a workflow that leveraged LLMs to help create design documents, sprint plans, and architecture specifications. For a while, it was perfect — all the magic of AI assistance but with proper structure and planning.

All Honeymoons Must End

I’d gotten clever with my architecture. Microservices everywhere! Clean separations! But as I wrapped up my third sprint, architectural drift had set in. A little compromise here, a tiny violation of domain boundaries there … death by a thousand tiny, pragmatic decisions.

No problem, I thought. We can just refactor everything now! Just me and my AI pair programmer, cleaning house and restoring order. At first, it was glorious. We were moving at lightning speed, reorganizing code across more than a dozen files simultaneously.

Then, without any worry or trepidation, I tried to start the FastAPI service that I built for my application’s back end.

Oh No!

Remember that scene in “Jurassic Park” when they realize the raptors have been testing the fences for weaknesses? That’s what my error messages felt like. Each fix spawned two new problems. My test coverage plummeted to barely 50%, and every attempt to improve it just revealed more issues. My AI assistant and I, previously an unstoppable duo, were now like perfect strangers trying to solve a Rubik’s cube in the dark while wearing oven mitts.

The humbling irony? The very tools that had made me feel invincible had just helped me dig the deepest development hole I’d experienced in years. I had moved too fast, changed too much, and completely overwhelmed both my own ability to reason about the system and my AI assistant’s capacity to help fix it. Had I forgotten that design validation for adherence, best practices, and patterns should be conducted in every sprint, rather than kicking the can down the road? Apparently, I had!

After days of playing whack-a-mole with errors, I had to make a tough call. The project needed a fresh start, this time with sounder design principles from day one. Sometimes, the best way out of a hole is to stop digging and climb out a different way entirely.

The lesson? For the uninitiated, AI-assisted development can be like strapping a rocket engine to a bicycle. It can help you move at incredible speeds, but strap a rocket to a bicycle with wobbly wheels, and, well … you can picture how that ends. The fundamentals of software engineering are now more important than ever: clean architecture; careful design; disciplined development practices; thorough testing. These aren’t constraints — they’re the foundations that let us safely use all this newfound power.

So embrace the magic of AI-assisted development, but don’t get drunk on the power like I did. Speed means nothing if you’re running in the wrong direction. And maybe, just maybe, think twice before refactoring a dozen files at once — no matter how invincible your AI sidekick makes you feel.

After all, the goal isn’t just to write code faster — it’s to build better software faster. Sometimes, you need to learn that lesson the hard way. I know I did.