Building With AI Code Generators: What Actually Works

AI coding tools have gotten good enough that non-technical people can build working software. Not just prototypes or demos, but actual tools that run internal business processes. I'm doing it with Toolpod, and I've learned some things about what makes the difference between code that works and code that falls apart.

The term floating around for this is "vibe engineering." You're not writing code from scratch, you're guiding an AI to write it for you. The structure matters more than you'd think.

Set Up CI/CD From Day One

This was the single best decision I made early. I use GitHub Actions with two branches: staging and main. Every change goes to staging first for testing, then merges to main when it works.

Having this workflow prevents so much chaos. Without it, you're constantly wondering if the last change broke something. With it, you test in staging, confirm it works, and promote to production with confidence.

It takes maybe an hour to set up initially. Worth every minute.

Write Down Your Rules

I created an agents.md file in my project. It's basically instructions for the AI about how to work on this specific codebase. Things like never use mock data, always validate API responses, document any workarounds for bugs we've hit before.

This prevents the AI from making the same mistakes twice. You tell it once, it remembers for the whole project. Way better than re-explaining the same constraint every time you ask it to add a feature.

Keep Functions Single-Purpose

AI tools sometimes create functions that do multiple things. If you have endpoints that create labels and the AI suggests an endpoint that also deletes them, push back. Keep your structure unambiguous.

This matters because when you ask the AI to fix something later, you want it to be obvious which function to change. If everything is tangled together, the AI might "fix" one thing and break another.

Be Specific When You Ask

The more context you give the AI, the better the output. Don't just say "add a user profile page." Say "add a user profile page with these fields, using this layout pattern, styled like the dashboard page, with validation on the email field."

Think of the context window as your budget. If you're vague, the AI guesses. If you're specific and include examples, it gets it right the first time. Screenshots help. Code snippets from other parts of your project help even more.

Make the AI Show Its Work

I always ask the AI to log what it's doing. Show me the API response. Show me what variables changed. Don't just say "done."

This is huge for debugging. When something doesn't work, you need to see where it went wrong. If the AI just tells you it's finished, you're flying blind. If it shows you the logs and the actual data, you can spot the problem immediately.

The Testing Problem

This is the part I'm still figuring out. Testing and debugging AI-generated code is harder than it should be.

When you're 20 steps into building a feature and something breaks, replicating that exact state to test a fix is painful. The AI can write the code fast, but debugging it step by step still takes time.

I've been trying to find a good workflow for this. Some kind of browser-based testing where the AI can run its own changes and verify they work before calling it done. Haven't found a great solution yet.

What I Use

For Toolpod, the stack is Next.js on Vercel. Completely free hosting, auto-deploys from GitHub, supports both static pages and API routes. The combination works really well for this kind of project.

I use Cursor as my AI code editor. There are other options now like Windsurf and Continue, but Cursor has been solid. The main thing is picking one and learning how to prompt it effectively for your specific project.

The Honest Part

Building with AI tools is not the same as being a developer. You can make working software, but you're limited by what you can clearly explain and what the AI can understand. Complex architectural decisions still require someone who knows what they're doing.

But for small tools, internal business apps, or side projects like Toolpod? It works. You can build real things that people use. You just have to take the workflow seriously and not treat it like magic.

Set up proper deployment pipelines. Document your decisions. Be specific in your prompts. Make the AI explain what it's doing. These things turn "this kind of works" into "this actually runs in production."

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