TL;DR: Vibe coding — prompt, paste, and hope — is great for quick prototypes. For larger projects, it quickly breaks down. The fix isn’t “more AI,” it’s better structure. Treat AI like a junior developer: give it context, guardrails, and a clear spec. That’s Spec-Driven Development — and it’s a game changer.
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Vibe Coding: Fun Until It Isn’t
When I first discovered AI coding tools, I fell into what I call the “vibe coding” phase. Need a login page? Prompt the AI. Need styling? Prompt again. Need to fix an error? Another prompt.
At first, using Cursor felt like magic. One or two prompts and boom — working code. My entire job seemed to be clicking the “Accept All” button. I felt like I’d discovered some kind of coding cheat code.
For about two weeks, I moved fast, building features that somehow worked. This approach is actually perfect for prototypes and demos — you can go from idea to working concept in hours.
But as my project grew, the cracks appeared.
When the Magic Breaks Down
The AI started making expensive mistakes. It would rewrite entire files incorrectly, create functions that already existed, break existing functionality while adding new features. Each new feature came back with different state management, inconsistent styling, and duplicated APIs.
My app started to look like a group project where nobody spoke to each other.
Not only did this slow down development, but it was burning through money fast. All those API calls for code I’d have to throw away and regenerate.
That’s when I realized I had to start reading the code myself. I couldn’t just blindly click “Accept All” anymore. This forced me to learn TypeScript and CSS — which was terrifying at first, but also meant I could start pointing the AI to specific files: “Look at this existing function” or “Follow the pattern in this component.”
The problem wasn’t the AI — it was me.
The Shift to Specs
In my frustration, I started reading about real software development practices: requirements gathering, design docs, architecture planning. The “serious” stuff I had always thought was overkill for side projects.
Wait. Actually, as a seasoned backend engineer, I already knew this stuff. I’d written design docs for distributed systems, gathered requirements for API features, planned database schemas. The practices weren’t foreign to me — I just hadn’t thought to apply them to frontend development.
Then it hit me: what if I applied these methods, but directed them toward AI?
I began treating AI like a capable but inexperienced teammate. Someone who’s technically skilled but needs proper direction and context.
Building Structure Into AI Development
So I started experimenting. I wrote down what I wanted: the problem, the users, what success looked like, how the feature should fit into the existing system. Then I gave the AI the context it needed: existing APIs, data structures, navigation flow, and design patterns.
The results were night and day.
Instead of vague prompts like “build user authentication,” I’d write detailed specifications:
- What the feature accomplishes and why
- How users interact with it step by step
- Technical requirements and integration points
- Clear acceptance criteria for “done”
Then I’d include project context: how my state management worked, what API patterns I was using, how I organized components.
The Context Breakthrough
Maintaining context became the single most important thing. Instead of letting the AI improvise, I created a living document of how my app worked — existing APIs, page structure, common types, styling patterns. It was like an employee handbook for the AI.
Suddenly, the code it produced matched my existing structure. Features that once took hours to stitch together now integrated smoothly in minutes.
I tested this approach on my second project. Before, adding comments to blog posts took four frustrating hours of back-and-forth. After, with a spec and context, the AI delivered a clean solution in half an hour. Same AI, same request — completely different outcome.
A Crucial Learning About AI Limits
Through all this, I discovered something important:
AI coding can’t accomplish things beyond your understanding. It can’t lift your ceiling, but it can raise your floor.
The AI can help you implement solutions you understand conceptually, even if you don’t know the exact syntax. But it can’t magically solve problems you don’t comprehend or build architectures you can’t conceptualize.
If I couldn’t understand why my state management was getting messy, the AI couldn’t fix it either — it would just make it messier in more sophisticated ways.
But if I understood what I wanted, even if I didn’t know how to implement it, the AI could bridge that gap.
Why Spec-Driven Development Works
This approach changed everything. My features stopped feeling like random add-ons. Development moved faster because I wasn’t constantly debugging mismatches. I understood my own code better. And most importantly, I stopped fighting the AI. It finally felt like we were working together.
At first, I thought I had stumbled onto something new. But I eventually realized this was just software engineering 101 — requirements, design, consistency — applied to AI development. The difference was that instead of managing humans, I was managing neural networks.
Requirements are still requirements. Design still matters. Context still needs to be shared. Code still needs to be consistent.
Who Benefits Most from AI Coding
After building two projects this way and watching others struggle (or succeed) with AI tools, I’ve noticed a pattern:
The people who get the most out of AI coding aren’t necessarily the best programmers. They’re the architects with product sense.
You don’t need to be a programming language expert, but you do need to understand systems: How do frontend and backend connect? How does data flow through an application? You need to grasp the big picture.
Product sense is crucial. The AI can write perfect code for the wrong feature. Having a sense for what makes good user experience is more valuable than knowing the latest framework.
You need to be able to read and evaluate code — not write it from scratch, but understand: Does this look reasonable? Does it follow our patterns? Will this be maintainable?
The AI handles the syntax and implementation details. You handle the architecture, user experience, and system design.
Lessons From 6 Months of Vibe Coding
- AI tools are incredibly powerful, but only with proper direction
- Context is everything — the AI needs to understand your project holistically
- Software engineering practices exist for a reason — they work with AI teams too
- You can build complex apps without deep frontend experience — but you need systematic thinking
The lesson? AI can be a powerful teammate, but only when you give it proper direction.
Looking Ahead
Managing specs and context manually was tedious. I often found myself copying and pasting details into prompts and trying to remember patterns from weeks before. That pain pushed me to start building tooling around this workflow.
I’ll share more about that journey in a future post — the tools I tried, what didn’t work, and how I ended up building something that made this workflow automatic.
For now, my advice is simple: don’t just vibe code. Take a moment to think like a project manager. Write down what you want to build, define how it should work, and keep track of the patterns in your codebase.
The future of software engineering won’t be about typing faster — it will be about thinking more clearly.
Spec-Driven Development isn’t just a better way to use AI tools. It’s the future of systematic software development.