AI-generated code is a double-edged sword.
On one hand, AI tools like Claude, Cursor, and GPT-4 have made it easier than ever for non-technical founders to build software. You can spin up an MVP in hours, not weeks. But on the other hand, if you don’t steer the AI properly, you can quickly end up with a monster - files with over 1000+ lines of code, all tangled together.
I’ve been talking to one founder in particular who’s running into this problem. He’s been using Claude to generate his product, and while it’s helped him move fast, he now has a giant, bloated codebase in a handful of huge files. Debugging is a nightmare, and making even small changes feels risky.
And he’s not alone. I’m seeing a lot of non-technical solopreneurs, producteers and indie hackers talking about this exact issue.
So what’s happening here, and how do you fix it?
Why Does This Happen?
The problem is AI momentum.
Once an AI is in full flow - especially when using agentic capabilities like in Cursor - it tends to keep adding more and more code to the same file.
Common pain points include:
• Index files growing massive, with multiple areas of functionality crammed in.
• AI struggling to debug because it can’t keep track of all the dependencies.
• Human debugging being easier in some cases, but intimidating when the file is too large.
• Risk of breaking everything when making a single fix, because the logic is too intertwined.
If you’ve found yourself staring at a wall of AI-generated code, wondering where to even start, here’s how to get back in control.
How to Stop AI From Creating Bloated Code Files
1️⃣ Step Back & Get AI to Refactor
Once AI has finished a task, don’t just move on - ask it to pause and analyze the file size.
📌 Prompt it to review the file holistically and suggest a better structure.
✅ What to ask:
“Break this file into smaller, logical components. Explain your approach, list the files you’ll create, and only proceed after I confirm.”
This forces the AI to think before it acts, which prevents it from mindlessly refactoring code in ways that make things worse.
2️⃣ Force AI to Work One Step at a Time
Large code changes fail when AI rushes ahead without feedback.
📌 Instead of asking it to do everything at once, break it down into steps.
✅ Better prompt structure:
“Here’s what I need: (1) Break the code into logical components. (2) Refactor each piece separately. (3) Reassemble them in an index file. Work on one part at a time, checking in with me after each step.”
AI performs better when given a checklist - this keeps it from overcomplicating the structure.
3️⃣ Forget everything and remember (expect bugs & test everything)
Once the AI has reassembled your files, assume something is broken.
🚨 Common AI refactoring issues:
• Misplaced dependencies (functions moved but not properly linked).
• Unintentional UI changes (AI might tweak frontend code without you realising).
• Silent logic errors (no errors in the console, but things don’t work as expected).
✅ What to do:
• Explicitly tell AI not to modify UI unless requested.
• Run thorough testing after each major AI change.
• Push to your git repo before AI refactors your code.
Test-Driven Development (TDD) - The Product Mindset Shift
As product people, we’re used to balancing speed with structure. If you’ve ever worked with senior engineering leads or been part of a team where Test-Driven Development (TDD) was standard practice, you know the mindset shift it requires.
TDD forces teams to think differently:
• You define success upfront. Before writing any code, you first define what “working” actually means.
• You build in small, iterative steps. Instead of dumping out large, risky changes, each step is validated by an automated test.
• You focus on maintainability. Clean, modular, and scalable code becomes the norm rather than an afterthought.
For solopreneurs and indie hackers, these principles can (and should) apply to AI-assisted development too. The challenge is that AI tools don’t naturally follow TDD unless you explicitly tell them to.
How to Get AI to Use TDD Properly
If you’re using AI to generate code, here’s how you can bake TDD into your workflow from the start:
✅ Start every coding task with this prompt:
“We are using Test-Driven Development (TDD). First, generate a set of unit tests for this feature. Then, write the minimal code necessary to pass those tests. Work one step at a time and confirm each test before proceeding.”
This forces the AI to think methodically, reduces bugs, and helps break down functionality into smaller, more manageable parts.
💡 Why does this matter for product builders?
• It reduces the fear of breaking things when iterating.
• It prevents AI from over-engineering solutions.
• It makes debugging easier later when you need to modify features.
If you’re a founder, indie hacker, or anyone building with AI, TDD isn’t just a development best practice - it’s a way to de-risk your product decisions.
Final Thoughts
AI is an incredible coding assistant, but it won’t always make the right architectural, or engineering decisions for you.
Non-technical founders using AI need to guide the process actively - otherwise, they risk ending up with an unmaintainable mess.
If you’re using AI to generate code, pay attention to when files start getting too big, and step in early to break them down. A few simple prompts can save you hours of debugging down the line.
🚀 Have you run into this problem? How did you handle it? Let’s discuss.