How to Debug Code Written by AI

AI coding tools like Claude and Cursor have made it much faster to write software. They can generate components, write API integrations, and help refactor large sections of a codebase. The speed is impressive, but the code they generate still needs careful review.

One of the most common mistakes developers make is assuming AI generated code is correct simply because it runs. In reality, AI tools can introduce subtle issues such as inconsistent patterns, incorrect assumptions about the codebase, or small logic mistakes that only show up later.

The goal is not to avoid AI generated code. The goal is to build a workflow that makes it easy to verify, review, and improve what the AI produces.

Over time I have found a few practices that make debugging AI written code much easier.

Start With Linting and Consistent Code Quality

The first thing I like to do in any project that uses AI coding tools is set up proper linting.

Linting gives you an automatic baseline for code quality. Tools like ESLint can detect common issues such as unused variables, inconsistent formatting, missing dependencies, and incorrect patterns before the code even runs.

AI tools often generate code that technically works but violates the project's style rules. Without linting, those inconsistencies slowly spread across the codebase.

A typical setup might include:

• ESLint for JavaScript and TypeScript rules
• Prettier for consistent formatting
• TypeScript strict mode for type safety
• Automated lint checks in pull requests

When these tools are configured correctly, they act as the first guard rail for AI generated code. Instead of manually reviewing every small formatting issue, the linter immediately highlights anything that does not follow the project's standards.

Structure the AI With Project Rules

Another important step is giving the AI clear instructions about how the project is structured.

Most AI coding tools are trained on general programming patterns. If you do not provide project specific guidance, the AI will often mix different styles or frameworks in ways that do not match your codebase.

This is where structured rule systems become useful.

In Cursor, for example, you can create project rule files that explain things like:

• Which framework the project uses
• Where components should live
• How data fetching works
• What coding patterns are allowed
• Which patterns should be avoided

These rules act like onboarding documentation for the AI. If you use Next.js, we wrote a practical guide on how to set up Cursor rules for Next.js projects that walks through exactly what to include.

This is also the idea behind ShipKit. Instead of starting with a blank project and trying to teach the AI everything from scratch, the architecture already includes clear conventions and patterns that AI tools can follow.

When the structure is predictable, the AI tends to generate much more reliable code.

Review AI Code Like You Would Review a Junior Developer

Even with linting and structured rules, code reviews are still important.

AI generated code should be reviewed with the same mindset you would use when reviewing code written by a junior developer. The code might be functional, but it may not always follow the best design choices for the project.

When reviewing AI code, I usually look for a few specific things:

• Does the logic actually solve the problem?
• Does the code match the patterns already used in the project?
• Are there unnecessary abstractions or duplicated logic?
• Is error handling present where it should be?

AI sometimes overcomplicates solutions or introduces layers that are not necessary. A quick review helps keep the codebase clean and maintainable.

Use AI Code Review Tools

Another layer of protection comes from automated code review tools.

Tools like CodeRabbit and Greptile analyze pull requests and highlight potential issues before code is merged. These tools act as a second set of eyes and can catch problems that are easy to miss during manual review.

Personally, I have used CodeRabbit and found it extremely useful. One of the advantages is that you can provide it with your project rules and guidelines. For example, you can include the same architectural rules that exist in ShipKit.

This allows the reviewer to check whether the code follows the project's standards. If the AI generates something that violates those rules, the review tool can flag it automatically.

Having multiple guard rails in place makes a big difference.

The workflow might look something like this:

1. AI generates the initial code
2. Linting catches formatting and structural issues
3. Automated review tools analyze the pull request
4. A developer performs the final review

Each step reduces the chance that problematic code reaches production.

Debug the Assumptions the AI Makes

One of the most important debugging techniques when working with AI generated code is understanding the assumptions the AI made.

AI tools sometimes guess details about your system. For example, they might assume a database schema, invent helper functions that do not exist, or import modules that are not part of your project.

When something breaks, the problem is often not the syntax but the assumption.

A few helpful debugging steps include:

• Verify every import actually exists
• Confirm API responses match what the AI expects
• Check that environment variables are correct
• Ensure database queries match the real schema

Once you get used to spotting these patterns, debugging AI generated code becomes much faster.

Keep AI Generated Code Small

Another useful strategy is keeping AI generated changes small.

Instead of asking the AI to build an entire feature at once, it is often better to generate smaller pieces of the implementation. This makes the output easier to review and easier to debug.

For example:

Bad workflow:

"Build the entire authentication system."

Better workflow:

• Generate the login API handler
• Generate the UI component
• Add validation logic
• Add error handling

Smaller steps make it easier to verify each piece before moving forward.

Build Guard Rails Into the Workflow

The most effective approach is combining multiple layers of protection.

AI tools are extremely powerful, but they work best when the environment around them is structured. Linting, project rules, automated reviews, and manual reviews all work together to keep the system stable.

When those guard rails are in place, AI becomes a powerful assistant rather than a source of unpredictable code.

Where to Go From Here

Debugging code written by AI becomes much easier when your project has clear structure and guard rails. Linting, review tools, and consistent conventions help catch problems early and keep generated code aligned with your architecture.

The biggest challenge is not the AI itself. It is the environment the AI is working in. When the project structure is inconsistent or undocumented, AI tools tend to generate code that drifts away from your standards.

That's why we built ShipKit.

ShipKit is a rule-driven Next.js architecture designed specifically for AI coding tools like Cursor and Claude. It provides clear project conventions, structured patterns, and guidance files that help AI assistants generate code that actually fits your codebase.

Once that structure is in place, you can move much faster when starting new projects.

That is where ShipUI comes in.

ShipUI is our collection of production-ready Next.js starter themes built on top of ShipKit. Each theme includes real components, real project structure, and everything wired up so you can begin building immediately.

ShipKit gives your AI tools the structure they need to write better code.
ShipUI gives you a clean, production-ready starting point so you can ship faster.

Buy once, own forever. Start building immediately.