Stop Cursor AI From Writing Broken Code

If you’ve spent more than a few hours building apps with Cursor or Replit AI, you’ve probably seen this happen.

You ask the AI to build a feature.

Instead, it:

• Creates files in random places.
• Rewrites code that was already working.
• Hallucinates imports.
• Keeps fixing one error only to create three more.
• Gets trapped in an endless “I fixed it” → “New error” loop.

Sound familiar?

You’re not alone.

The biggest mistake most developers make isn’t using Cursor. It’s asking Cursor to code before giving it a proper architecture.

The Real Problem Isn’t Cursor

Cursor is incredibly good at writing code.
But it’s terrible at guessing your project structure.

When the AI doesn’t know:

• the tech stack,
• folder organization,
• database design,
• coding standards,
• or implementation order,

it starts making assumptions.

Those assumptions become:

• duplicate components,
• inconsistent APIs,
• broken imports,
• conflicting state management,
• endless refactors,
• and eventually an agent that keeps chasing its own mistakes.

This is why people think Cursor is “hallucinating.”

Most of the time, it’s simply missing context.

The Core Realization

Think of Cursor as a junior software engineer.

Would you tell a junior developer:

“Go build my entire SaaS app.”

Of course not.

You’d first provide:

• Architecture
• Requirements
• Folder structure
• Database design
• Development plan

Only then would they start coding.

AI code builders work exactly the same way.

They perform dramatically better when they’re given a complete system architecture before generating a single file.

That one change eliminates a surprising number of AI-generated bugs.

The 3-Step Workflow That Changed Everything

Instead of asking Cursor to “build the app,” break the process into three phases.

Step 1 — Define the Tech Stack and Database Schema

Before writing any code, document the foundation.

Include things like:

• Frontend framework
• Backend framework
• Database
• Authentication
• State management
• API style
• Deployment target

Example:

Frontend:
- Next.js
- TypeScript
- Tailwind CSS

Backend:
- Node.js
- Express

Database:
- PostgreSQL
- Prisma ORM

Authentication:
- Clerk

Hosting:
- Vercel

Enter fullscreen mode Exit fullscreen mode

Then define your database schema.

Example:

Users
- id
- email
- password_hash
- created_at

Projects
- id
- user_id
- title
- created_at

Tasks
- id
- project_id
- title
- completed

Enter fullscreen mode Exit fullscreen mode

Now Cursor understands what data exists before it starts inventing tables.

Step 2 — Design the Directory Structure

This step is skipped surprisingly often.

That’s exactly why AI starts dumping files into the root directory.

Instead, define the project layout first.

Example:

src/
├──components/
├──pages/
├──hooks/
├──services/
├──lib/
├──utils/
├──types/
├──styles/
├──database/
├──prisma/
├──api/
├──tests/

Enter fullscreen mode Exit fullscreen mode

Also define rules such as:

Shared components belong in components/
API logic stays inside services/
Database code only lives in database/
Utility functions stay inside utils/

This removes a huge amount of ambiguity.

The AI no longer has to guess where new files belong.

Step 3 — Build Using a Phased Prompt Sequence

This is the biggest improvement you can make.

Never ask Cursor to build everything at once.

Instead, split development into separate prompts.

Phase 1 — Build the UI

Prompt only for:

• pages
• layouts
• reusable components
• navigation
• styling

Don’t mention authentication.

Don’t mention APIs.

Don’t mention databases.

Just the interface.

Phase 2 — Add Authentication and Database

Once the UI is complete, move to:

• authentication
• database schema
• migrations
• API routes
• CRUD operations

Since the UI already exists, Cursor has a stable target to connect everything to.

Phase 3 — Add Business Logic

Only after the app structure is stable should you implement:

• validation
• workflows
• automation
• permissions
• notifications
• background jobs
• AI integrations
• third-party APIs

Each phase builds on the previous one.

Nothing gets rewritten unnecessarily.

Why This Works

Large Language Models have limited working memory.

The more unrelated tasks you ask them to solve simultaneously, the more likely they are to:

• lose context,
• overwrite existing files,
• generate inconsistent code,
• or loop while trying to repair previous mistakes.

Breaking development into clear phases reduces complexity.

Instead of solving twenty problems at once, the AI solves one well-defined problem at a time.

The result is cleaner code, fewer regressions, and a much smoother development experience.

Common Prompting Mistakes

Avoid prompts like :

Build a complete production-ready SaaS with authentication, payments, dashboard, admin panel, AI chat, Stripe, email verification, analytics, and deployment.

That’s too many responsibilities in one request.

A better prompt is:

Build only the dashboard UI using the provided architecture. Do not create authentication, APIs, or database code. Follow the directory structure exactly.

The second prompt is far more constrained, which makes the output more reliable.

A Simple Rule to Remember

Don’t ask AI to design and build at the same time.

First, give it the blueprint.

Then let it write the code.

That small change can save hours of debugging and reduce the chances of Cursor getting stuck in repetitive fix loops.

Resources

• Open-source GitHub Workflow: View on GitHub
• Copy-paste Master Prompt Sequence: Get Full Prompt on PromptBase

The PromptBase collection includes structured prompts designed to help Cursor, Replit Agent, Bolt.new, and similar AI coding tools generate cleaner, more maintainable projects with fewer architectural mistakes.

Final Thoughts

AI-assisted development is evolving quickly, but the fundamentals of software engineering haven’t changed.

Good architecture still comes before good code.

The better your specifications, folder structure, and implementation plan, the better your AI-generated output will be.

Cursor isn’t failing because it’s incapable.

It’s usually failing because it’s being asked to improvise an entire software architecture while simultaneously writing the implementation.

Give it a plan first.

Then let it build.

Over to You

How do you structure your Cursor or Replit workflow?

Do you use architecture documents, phased prompts, or another system that keeps AI from going off the rails?

Share your workflow in the comments—I’d love to see what has worked for you.