How to Use AI for Code Review: Prompts + Workflow Guide

Code review is essential but time-consuming. AI can help—not by replacing human reviewers, but by catching common issues, enforcing standards, and letting humans focus on higher-level concerns. If you're looking for broader coding assistance, check out our ultimate guide to coding prompts.

This guide covers how to integrate AI into your code review workflow with battle-tested prompts.

Why Use AI for Code Review?

AI's Strengths in Review

• Consistency: Never forgets to check something
• Speed: Reviews in seconds, not days
• Pattern Matching: Excellent at spotting common issues
• Documentation: Can explain why, not just what
• 24/7 Availability: No time zones or waiting

AI's Limitations

• Doesn't understand business context without explicit explanation
• May miss architectural implications of changes
• Can't evaluate team dynamics or code ownership
• May produce false positives on unusual patterns
• Doesn't know your internal conventions unless told

The key is using AI for what it's good at while keeping humans in the loop for judgment calls.

The AI Code Review Workflow

Step 1: Pre-Review Screen

Before deep review, use AI to catch obvious issues:

Review this code for obvious issues:
- Syntax errors
- Missing imports
- Undefined variables
- Basic security red flags (exposed secrets, SQL injection risks)

Just list issues found. If nothing obvious, say "No obvious issues."

[CODE]

This quick scan catches low-hanging fruit instantly.

Step 2: Category-Specific Reviews

Run focused reviews for specific concerns:

Security Review:

You are a security engineer. Audit this code for:

1. Injection vulnerabilities (SQL, XSS, command)
2. Authentication/authorization flaws
3. Data exposure risks
4. Cryptographic weaknesses
5. Input validation gaps

For each issue:
- Line number
- Severity (Critical/High/Medium/Low)
- Specific vulnerability
- Recommended fix

[CODE]

Performance Review:

Analyze this code for performance issues:

1. Time complexity concerns (O(n²) or worse)
2. Unnecessary database queries (N+1 problems)
3. Memory leaks or excessive allocation
4. Blocking operations that could be async
5. Missing caching opportunities

[CODE]

Context: [Any relevant context about scale/usage]

Best Practices Review:

Review for [LANGUAGE] best practices:

1. Naming conventions
2. Code organization
3. Error handling
4. Testing considerations
5. Documentation gaps

Our style guide: [Reference or key points]

[CODE]

Logic Review:

Review this logic for correctness:

1. Edge cases not handled
2. Off-by-one errors
3. Incorrect conditional logic
4. Race conditions in concurrent code
5. State management issues

Expected behavior: [What the code should do]

[CODE]

Step 3: Consolidated Summary

After specific reviews, create a summary:

I've reviewed this code across security, performance, and best practices.

Combine my notes into a single PR review:

[PASTE YOUR NOTES]

Format as:
## Summary
[One paragraph overview]

## Required Changes
[Things that must be fixed]

## Suggestions
[Nice-to-have improvements]

## Tests Needed
[Test coverage recommendations]

## Questions for Author
[Things that need clarification]

Prompts for Common Review Scenarios

PR Review Template

Review this pull request:

PR Title: [TITLE]
Branch: [feature/branch-name] -> [main]
Author: [AUTHOR]
Context: [What this PR does]

Changes:
[CODE DIFF OR NEW CODE]

Provide:
1. Overall assessment (approve/request changes/discuss)
2. Security concerns if any
3. Performance implications
4. Code quality feedback
5. Test recommendations

Tone: Constructive and specific. Praise what's done well.

Refactoring PR Review

This PR refactors [COMPONENT] for [GOAL].

Review to ensure:
1. Functionality is preserved (no behavioral changes)
2. The refactoring actually improves [GOAL]
3. No new issues introduced
4. Tests still pass and remain relevant

Old implementation:
[OLD CODE]

New implementation:
[NEW CODE]

Confirm the refactoring is correct and actually beneficial.

Feature PR Review

This PR adds a new feature: [FEATURE DESCRIPTION]

Evaluate:
1. Does the implementation match the requirements?
2. Are edge cases handled?
3. Is error handling comprehensive?
4. Is the feature testable and tested?
5. Are there any security implications?
6. How does this integrate with existing code?

Requirements:
[REQUIREMENTS OR SPEC]

Implementation:
[CODE]

Bug Fix PR Review

This PR fixes: [BUG DESCRIPTION]

Review the fix:
1. Does it actually fix the bug?
2. Could it introduce new bugs?
3. Is the fix minimal and targeted?
4. Should tests be added to prevent regression?
5. Is the root cause addressed (or just symptoms)?

Bug report:
[DESCRIBE THE BUG]

Fix:
[CODE CHANGES]

Browse more code review prompts in our library.

Language-Specific Review Prompts

JavaScript/TypeScript

Review this TypeScript code:

[CODE]

Check for:
- Type safety issues (any usage, poor type definitions)
- Potential runtime errors
- React-specific issues if applicable (stale closures, missing deps)
- Modern ES6+ patterns vs legacy approaches
- Proper async/await usage

Stack context: [React/Node/etc.]

Python

Review this Python code:

[CODE]

Check for:
- PEP 8 violations
- Type hint opportunities/issues
- Pythonic vs non-pythonic patterns
- Exception handling
- Resource management (context managers)
- Common Django/FastAPI patterns if relevant

Python version: [VERSION]
Framework: [FRAMEWORK]

Go

Review this Go code:

[CODE]

Check for:
- Error handling (returning vs ignoring errors)
- Goroutine leaks
- Race condition risks
- Idiomatic Go patterns
- Proper resource cleanup (defer)
- Interface design

SQL

Review this SQL:

[QUERY]

Check for:
- SQL injection risks (if input-sourced)
- Performance (missing indexes, full table scans)
- Correctness (joins, aggregations)
- Best practices (explicit column selection, etc.)

Tables involved: [SCHEMA INFO]
Expected data volume: [ROW COUNTS]

Integrating AI into Your Review Process

Team Workflow Option 1: AI as First Pass

Developer Opens PR
       ↓
AI Review (automated or triggered)
       ↓
Developer addresses AI feedback
       ↓
Human reviewer focuses on:
- Business logic
- Architecture decisions
- Team conventions
- Knowledge transfer

Benefit: Humans spend less time on mundane issues.

Team Workflow Option 2: AI as Checklist

Human reviewer uses AI prompts for specific checks:
- "Check security" prompt
- "Check performance" prompt
       ↓
Human synthesizes findings
       ↓
Human adds context-aware feedback

Benefit: Thorough coverage without bottleneck.

Team Workflow Option 3: AI-Assisted Self-Review

Developer writes code
       ↓
Developer runs AI review on own code
       ↓
Developer fixes issues before PR
       ↓
Cleaner PRs for team review

Benefit: Faster review cycles, developer learning.

Using AI in IDE Code Review

Cursor

Create a .cursorrules file for consistent reviews:

When reviewing code:
1. Always check for security issues first
2. Follow our style guide: [link]
3. Suggest tests for complex logic
4. Keep feedback actionable and specific

Then use inline comments to trigger reviews:

# Review this function for performance
def process_large_dataset():
    ...

Windsurf

Similar approach with .windsurfrules:

Code review priorities:
1. Security > Performance > Readability
2. Always explain the "why" behind suggestions
3. Reference official documentation when relevant

Browse our rules library for complete configurations. For an in-depth comparison, see our guide on Cursor vs Windsurf vs Claude Code.

Handling AI Review Limitations

When AI Gets It Wrong

AI will sometimes:

• Flag valid patterns as issues
• Miss context-dependent problems
• Suggest changes that don't apply to your situation

Response:

1. Verify suggestions before acting
2. Use your judgment—you know your codebase
3. Feed back to improve future prompts

Calibrating Sensitivity

If AI is too harsh:

Review this code at a HIGH bar—only flag actual issues, not style preferences.

If AI is too lenient:

Be extremely thorough. Better to flag false positives than miss real issues.

Adding Context AI Lacks

Context for review:
- This is a prototype, not production code
- Performance is not critical here
- We're migrating from [old pattern] so some legacy remains
- [Any other relevant context]

With this in mind, review:
[CODE]

Measuring AI Review Effectiveness

Track these metrics:

Metric	How to Measure
Issues caught by AI	Log AI suggestions that were acted on
False positive rate	Track suggestions that were dismissed
Time saved	Compare review times before/after AI
Bugs prevented	Track bugs caught in review vs production
Developer satisfaction	Survey team on AI review usefulness

Frequently Asked Questions

Does AI code review replace human reviewers?

No. AI handles mechanical checks; humans provide judgment, mentorship, and context-aware feedback.

What about code confidentiality?

Consider:

• Self-hosted solutions for sensitive code
• Privacy policies of AI providers
• Anonymizing code for review (remove company-specific names)

Should every PR go through AI review?

It depends on your workflow. Options:

• All PRs through AI first
• Only PRs with code changes (skip docs-only)
• Developer-optional AI self-review

Which AI is best for code review?

Claude 3.5 Sonnet is excellent for detailed review. GPT-4 is also strong. For IDE integration, Cursor and Windsurf work well. Try what fits your workflow.

How do I get my team to adopt this?

1. Start with a pilot—one sprint or one dev
2. Show time savings
3. Make it optional at first
4. Share prompts that work well
5. Iterate based on feedback

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Explore our complete coding prompts library for more code review and development prompts. Check our rules library for AI coding assistant configurations.