How to Review AI-Generated Code: A Practical Checklist for Catching Over-Engineering and Bugs | ZipBuild Blog

The Real Problem with AI-Generated Code

You ask Claude Code to build an authentication flow. It generates 400 lines of solution that looks impressive on the surface, has proper TypeScript types, and follows design patterns. Two days later, you realize it doesn't handle edge cases correctly, requires three database queries instead of one, and introduces a security vulnerability you almost missed.

This is happening across teams right now. Developers are shipping code generated by Claude, Cursor, and other AI assistants without proper review processes. The code compiles, tests pass, and then production breaks in ways that feel impossible to debug.

The fundamental issue: AI doesn't optimize for simplicity or your specific domain knowledge. It optimizes for plausible output. A function that works 95% of the time and handles 10 edge cases you didn't ask for is worse than a 50-line solution that handles your exact use case.

You need a review process specifically designed for AI-generated code.

Why Standard Code Review Misses AI Problems

Traditional code review catches logic errors and style issues. But AI-generated code has unique failure modes:

Overuse of design patterns that don't apply to your problem. You asked for user authentication, and you got a Strategy pattern, Factory pattern, and Observable layer. None of this was necessary.

Domain knowledge gaps. AI doesn't know your database constraints, your third-party API limitations, or your team's performance budgets. It generates code that "should work" but violates your actual system constraints.

Hidden bugs in common patterns. AI excels at boilerplate but struggles with subtle bugs in error handling, race conditions, and state management. Code that looks correct often has bugs hiding in catch blocks or async flows.

Over-parameterization. Functions accept 7 optional parameters when they could be split into 3 focused functions. This creates complexity debt.

Your review process needs to target these specific failure modes.

The AI Code Review Checklist

Use this checklist every time you receive generated code, whether from Claude Code, Cursor, or any AI assistant.

### 1. Simplicity First - Can This Be Cut in Half?

Read through the entire solution. Ask: could I write this in fewer lines without losing functionality?

If the answer is yes, the AI over-engineered. This is your most common problem.

Look for:

Multiple abstraction layers when one would work

Helper functions that are called once

Configuration objects instead of direct parameters

Middleware or decorator patterns where a simple condition would work

Example: AI generates an event emitter system for component communication when passing props or using a Context hook would work fine.

Action: Ask the AI to "simplify this to the minimal version that solves my problem." Make it non-negotiable.

### 2. Domain Violation Check - Does This Break Your Constraints?

Sit with your codebase for 5 minutes. What are your actual constraints?

How many database queries is your budget per request? (AI might generate N+1 queries without realizing)

What's your third-party API rate limit? (AI might call it in a loop)

What authentication model are you actually using? (AI might assume OAuth when you're using sessions)

What's your database schema? (AI might reference columns that don't exist)

Walk through the generated code and trace every external call, database operation, and API request. Ask: does this respect my system constraints?

Real example: Supabase with Row Level Security. AI generates code that queries the database 10 times in a page load because it doesn't understand that RLS requires separate client instances or server-side batching.

### 3. Error Handling - Does It Fail Gracefully or Explode?

The weakest part of AI-generated code is error handling. Look specifically at:

What happens if the API times out?

What happens if the database connection drops mid-transaction?

What happens if a third-party service returns unexpected data?

What happens if user input violates a constraint?

AI often generates happy-path code with generic catch blocks that don't actually handle anything.

Action: Rewrite error handling to fail explicitly and provide context. Don't accept "catch (e) { throw e }".

### 4. Performance Trace - Spot the Hidden Queries

For database or API code, trace the execution path:

How many database queries does this generate for a typical user action?

How many API calls for one page load?

Are there any queries in loops?

Are queries batched or individual?

Example Supabase mistake: fetching user data, then fetching related posts, then fetching comments for each post (N+1 problem). The code is clean and readable. It's also slow.

Walk through the code with realistic data and count the operations.

### 5. Type Safety Check - Is It Actually Type Safe?

Generated TypeScript often has subtle type safety issues:

`any` types hidden in callbacks

Assuming data shapes without validation

Missing null checks in optional fields

Implicit type coercion in comparisons

Look for:

Functions that accept `any` type

API response handling that doesn't validate structure

Database queries that assume schema matches code assumptions

These aren't syntax errors. They're runtime bombs waiting to happen.

### 6. The "Would I Ship This?" Test

If you wouldn't write it this way, it's not production-ready.

Ask yourself:

Would I defend this in code review?

Would I explain this to a junior developer without feeling embarrassed?

Would I be able to modify this in 6 months?

Is this simpler than the alternative?

If the answer to any of these is no, it's over-engineered or wrong.

Common Patterns That Signal Problems

Watch for red flags when reviewing AI-generated code:

Heavy use of generics where concrete types would work: `const handler = <T extends Record<string, any>>(data: T)` when the data shape is known

Extensive configuration objects when defaults would work: `const db = new Database({ poolSize: 10, maxRetries: 3, timeout: 5000, ... })` when one parameter matters

Multiple layers of abstraction for simple problems: repository pattern, service layer, and adapter for reading a user profile

Middleware chains where a simple conditional works

Complex state management for simple state

These aren't always wrong. But they're wrong 80% of the time in real production code.

What To Do When You Find Problems

When your review uncovers over-engineering or bugs:

Ask the AI to simplify specifically: "This has 3 abstraction layers. Rewrite it as a single function that does the same thing."

Add your constraints: "My API has a 100ms timeout and costs money per request. Rewrite this to minimize calls."

Validate the fix: Generate test cases for edge cases you found.

Don't accept "this is more flexible" as a reason to keep complex code. You don't need flexibility for problems you don't have.

The key is treating AI-generated code as a starting point, not a finished solution. Your domain knowledge and production constraints matter more than the AI's pattern library.

Build With Confidence

The teams shipping the best products with AI assistance aren't the ones who trust generated code completely. They're the ones with strong review processes and clear standards.

If you're building a full-stack app with Next.js, Supabase, or Claude Code, you're making architectural decisions that compound over time. A project scaffold that enforces structure and patterns from day one prevents most of these problems.

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