AI Code Review: Using Claude to Improve Your Code

Code review is where good code becomes great code. It catches bugs before production. It enforces standards. It spreads knowledge across teams.

AI can now do a lot of this. Not replace human review entirely, but augment it dramatically.

Here’s how to use AI for effective code review.

Why AI Code Review Works

Traditional code review has problems:

Reviewers are busy and rushed
Reviews are inconsistent (depends who reviews)
Context takes time to rebuild for each review
Tedious issues (formatting, naming) distract from important ones

AI addresses these:

Always available, never rushed
Consistently applies the same standards
Can read the entire codebase for context
Handles the tedious stuff so humans focus on the important stuff

The result: faster reviews, more thorough feedback, and humans freed to think about architecture and design.

Setting Up AI Code Review

Option 1: Manual Review Requests

The simplest approach. Paste code into Claude and ask for review.

“Review this code for bugs, security issues, and improvements:
[paste your code]
```"

Good for: ad-hoc reviews, learning, personal projects.

Option 2: Integrated in Claude Code

When working in Claude Code, ask for review as part of your workflow.

“I just finished implementing the payment processing feature. Before I commit, can you review the changes for security issues, error handling, and code quality?”

Claude can access the files and review them in context.

Option 3: Automated PR Review

For teams, set up automated review on pull requests. Several tools integrate Claude into GitHub/GitLab PR workflows.

When a PR is opened, Claude:

Reads the diff
Analyzes changes
Posts comments on specific lines
Summarizes the review

Good for: teams wanting consistent baseline reviews on all PRs.

What to Ask For

Different review focuses catch different issues.

Security Review

“Review this authentication code for security vulnerabilities. Check for:
SQL injection
XSS vulnerabilities
Authentication bypasses
Sensitive data exposure
Insecure defaults”

Performance Review

“Review this code for performance issues. Check for:
N+1 query problems
Unnecessary re-renders
Memory leaks
Inefficient algorithms
Missing caching opportunities”

Maintainability Review

“Review this code for maintainability. Check for:
Unclear naming
Functions that are too long
Missing error handling
Code duplication
Lack of comments on complex logic”

Best Practices Review

“Review this React code for best practices. Check for:
Proper hook usage
Component structure
State management patterns
Accessibility
Following React conventions”

Full Review

“Do a comprehensive code review. Check for bugs, security issues, performance problems, and code quality. Prioritize issues by severity.”

Reading AI Review Feedback

AI review output typically includes:

Critical issues: Bugs, security vulnerabilities, logic errors. Fix these before merging.

Warnings: Potential problems, edge cases, things that might break. Evaluate and usually fix.

Suggestions: Improvements that aren’t required. Consider implementing.

Style notes: Naming, formatting, conventions. Apply if they match your standards.

Questions: Things the AI isn’t sure about. Answer these - they often reveal unclear code.

Real Review Example

Here’s code I asked Claude to review:

async function processOrder(orderId) {
  const order = await db.orders.findById(orderId);
  const user = await db.users.findById(order.userId);

  if (user.balance >= order.total) {
    user.balance -= order.total;
    await db.users.save(user);
    order.status = 'paid';
    await db.orders.save(order);
    return { success: true };
  }
  return { success: false };
}

Claude’s review:

Critical Issues:

No transaction handling: If saving the user succeeds but saving the order fails, money is deducted but the order isn’t marked paid. This needs to be wrapped in a database transaction.
No null checks: If orderId doesn’t exist, order is undefined and order.userId throws. Same for user.
Race condition: Two simultaneous requests for the same user could both pass the balance check before either deducts, resulting in negative balance.

Warnings:

No error handling: Database operations can fail. Wrap in try/catch with proper error responses.
No input validation: orderId isn’t validated. Could be injection risk depending on DB.

Suggestions:

Add logging: Log payment attempts for audit trail.
Use optimistic locking: For the user balance to prevent race conditions.
Return error messages: “{ success: false }” doesn’t tell the caller why.

This took Claude about 5 seconds. It caught issues that would have caused real bugs in production.

Review Patterns That Work

The “Before Commit” Review

Before committing significant changes:

“I’m about to commit these changes to the payment module. Review for anything that could cause issues in production.”

Catch problems before they enter the codebase.

The “Fresh Eyes” Review

When you’ve been staring at code too long:

“I’ve been working on this for hours and I’m not sure if it’s good. Give me a fresh perspective on the overall approach and any issues you see.”

AI doesn’t get tired or develop blind spots.

The “Learning” Review

For educational feedback:

“Review this code and explain why any issues you find are problems. I want to learn, not just fix.”

Good for junior developers or learning new languages.

The “Comparative” Review

When choosing between approaches:

“Here are two implementations of the same feature. Compare them in terms of maintainability, performance, and correctness. Which would you recommend and why?”

Get objective analysis of trade-offs.

The “Checklist” Review

For structured review against specific criteria:

“Review this code against these criteria:
All error cases handled
Input validation complete
No sensitive data logged
All database calls in transactions
Unit tests cover main paths”

Integrating AI Review Into Your Workflow

Solo Developer Workflow

Write code
Before committing, paste changes into Claude for review
Address critical and warning issues
Consider suggestions for next iteration
Commit

This adds 5-10 minutes but catches problems early.

Team Workflow

Developer opens PR
Automated AI review runs immediately
Developer addresses AI comments
Human reviewer focuses on architecture, design, context
Merge

AI handles the tedious; humans focus on judgment.

Learning Workflow

Write code
Submit for AI review with “explain why” mode
Learn from feedback
Revise
Submit again until clean

Accelerates skill development.

Common AI Review Limitations

Context gaps: AI might not know your specific business rules or why certain decisions were made.

False positives: Sometimes AI flags things that are actually fine for your situation.

Overconfidence: AI presents opinions as facts. Not every suggestion is correct.

Scope blindness: AI reviews what you show it. It doesn’t know about related code changes.

Security limits: For truly critical security review, expert human review is still essential.

Making the Most of AI Review

Provide context: Tell Claude about your project, conventions, and why you made certain choices.

Ask specific questions: “Is this secure?” gets better answers than “Review this.”

Don’t blindly accept: Evaluate each suggestion. Some won’t apply to your situation.

Review the review: If AI suggests something that seems wrong, ask it to explain its reasoning.

Iterate: Review, fix, review again. The second pass catches new issues the fixes might introduce.

Master AI Code Review

Learn advanced techniques for AI-assisted code review and quality improvement.

Get the Course

The Compound Effect

Regular AI code review creates a compound effect:

You catch bugs earlier (cheaper to fix)
You learn patterns from repeated feedback
Your code quality baseline rises
You develop intuition for what AI will flag

After a few months of consistent AI review, you’ll start writing code that passes review on the first try. The AI trains you to think about edge cases, security, and performance as you write.

That’s the real value. Not just catching problems, but preventing them from being created in the first place.

Start reviewing your code with AI today. The improvement compounds faster than you expect.