20 tested prompts across 4 stages. Works with ChatGPT, Claude, and Gemini.

Most people try to use AI for ChatGPT for Code Review with a single vague prompt and get generic results. This guide takes a different approach: 4 targeted stages, from Set up the review through Write review feedback, each with a prompt that gives the AI exactly the context it needs. Use ChatGPT to catch bugs, improve code quality, and prepare better code before human reviewers see it. Works with ChatGPT, Claude, and Gemini.
Stage 1
Start here to frame what a good review looks like for this specific change.
Request a general review
Review this code for overall quality: [PASTE CODE]. Check for: bugs, edge cases, security issues, readability, and anything that would fail a production code review. Prioritize by severity.
Review a specific concern
Review this code specifically for [SECURITY VULNERABILITIES / PERFORMANCE ISSUES / EDGE CASES / READABILITY]: [PASTE CODE]. Be specific and give line-level feedback.
Review a pull request
Review this code change: [PASTE THE DIFF OR DESCRIBE WHAT CHANGED]. Is the change correct? Are there bugs or missing edge cases? Is the approach appropriate for the goal?
Create a review checklist
Create a code review checklist for [TYPE OF CODE: REST API / REACT COMPONENTS / DATABASE QUERIES / BACKGROUND JOBS]. What should every reviewer check for in this type of code?
Review for your team's standards
Review this code against these standards: [LIST YOUR STANDARDS OR CODING PRINCIPLES]. Flag specific violations and suggest what to change.
Stage 2
These prompts help you identify logic errors, edge cases, and security concerns.
Find logic bugs
Find logic bugs in this code: [PASTE CODE]. For each bug, show the line, what it currently does, what it should do, and how to fix it.
Find edge cases
What edge cases does this code not handle: [PASTE CODE]? For each, describe what would happen and how to handle it.
Find security vulnerabilities
Review this code for security vulnerabilities: [PASTE CODE]. Check for: injection attacks, authentication issues, sensitive data exposure, insecure operations, and OWASP Top 10 issues.
Find performance issues
Review this code for performance problems: [PASTE CODE]. Look for: N+1 queries, inefficient loops, missing indexes, unnecessary re-renders, and blocking synchronous operations.
Review error handling
Review the error handling in this code: [PASTE CODE]. What errors are not handled? What fails silently? What error messages are too vague to diagnose a problem?
Stage 3
Use these prompts to suggest improvements to readability, structure, and maintainability.
Review readability
Review the readability of this code: [PASTE CODE]. Which parts would confuse a new developer? Flag unclear names, complex logic, and functions that are too long.
Review tests
Review these tests: [PASTE TESTS AND IMPLEMENTATION]. Are they testing the right things? What is not covered? Would these tests catch the most likely bugs?
Check for over-engineering
Is this code over-engineered for the problem it solves: [PASTE CODE]? What abstractions or complexity can be removed while still meeting the requirements?
Review API design
Review the function/API design in this code: [PASTE CODE]. Are the interfaces intuitive? Are function signatures clear? Is there anything a consumer would find confusing?
Review for consistency
Compare this new code to this existing code: [PASTE BOTH]. Is the new code consistent in style, naming, and patterns? What would need to change to fit in?
Stage 4
These prompts help you communicate your findings clearly and constructively.
Write constructive feedback
Help me write constructive code review feedback for this issue: [DESCRIBE THE ISSUE]. Write it professionally, explaining the why, not just the what.
Write inline comments
Write code review inline comments for these specific sections: [PASTE CODE WITH SECTIONS HIGHLIGHTED]. Each comment should be clear, specific, and actionable.
Write a review summary
Write a code review summary for these findings: [LIST FINDINGS]. Give a high-level view, categorize by severity, and set clear expectations for what must change.
Write an approval
Write a code review approval comment for this PR: [DESCRIBE THE PR]. It is ready to merge but I want to leave helpful non-blocking suggestions. Approve clearly while leaving the door open for improvements.
Request changes politely
I need to request changes on this code. Issues: [LIST ISSUES]. Write the request-for-changes comment professionally, explaining each issue without sounding like a rejection.
ChatGPT is consistent and tireless. It catches the same edge cases and security issues in every function, without getting fatigued. Humans tend to skim repetitive code — ChatGPT reads every line. It is also good at spotting common bug patterns from its training on millions of codebases.
No. Use AI as a first pass to catch obvious issues before human review. Human review adds value through knowledge of business context, team standards, and architectural judgment. The two complement each other well.
Avoid pasting code that contains secrets, credentials, or personal user data. For sensitive codebases, use a local AI tool or scrub sensitive values before sharing. Ask your organization about its AI usage policies.
Yes. Use ChatGPT to review your own code before submitting it and you will start seeing the patterns it catches. Over time, you will internalize better code quality instincts and catch more issues in your own code before writing it.
Give ChatGPT context: what the code is supposed to do, any constraints (security requirements, performance targets), and any specific concerns you already have. The more context, the more targeted and useful the review.
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