The Best AI Prompts for Code Review (Works with Any AI Tool)

The difference between a useful AI code review and a mediocre one usually comes down to the prompt, not the model. A generic "review this code" gets you generic feedback. A focused prompt — security review, performance analysis, naming and readability — gets you a review that actually catches something.

These prompts work with any AI tool: ChatGPT, Claude, Copilot Chat, Gemini. You can save your code review prompts for every PR instead of rewriting them each time. I've been using versions of these for about a year and they've caught real bugs. None of them require any specific context about your codebase — paste your code, run the prompt, get feedback.

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General Code Review

1. Comprehensive review

Review the following code for: (1) bugs and logic errors, (2) security vulnerabilities, (3) performance issues, (4) readability and naming. For each issue, point to the specific line or section and suggest a concrete fix.

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2. Quick scan — what stands out

I'm going to paste some code. Without going deep, tell me the 3-5 things that stand out most as potential problems. Don't give me an essay — just flag what catches your eye.

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3. Senior engineer review

Review this code as a senior engineer doing a real code review. Be direct and specific. Point out what's genuinely wrong, what's debatable, and what's fine. If something is subjective, say so. Don't pad the response with praise.

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4. Review for a junior developer

Review this code in a way that helps a junior developer learn. Explain not just what to change but why it matters. Balance encouragement with honesty — point out what's done well alongside what needs improvement.

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Security Review

These prompts are worth running on any code that handles user input, authentication, or sensitive data.

5. Security audit

Audit this code for security vulnerabilities. Check for: SQL injection, XSS, CSRF, insecure deserialization, broken authentication, improper input validation, hardcoded secrets, and any OWASP Top 10 concerns. Explain each issue and how to fix it.

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6. Authentication and authorisation review

Review this code specifically for authentication and authorisation issues. Are permissions checked correctly? Are there any privilege escalation paths? Is session handling secure? Is user input trusted when it shouldn't be?

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7. Data handling security

Review how this code handles sensitive data. Is it encrypted at rest and in transit? Is it logged anywhere it shouldn't be? Are there any places where sensitive data could be exposed in errors or responses?

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8. Input validation review

Review the input validation in this code. Is all user input validated and sanitised before use? Are there any paths where unvalidated input reaches a database, shell command, or file system?

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When a review turns up an actual bug, our debugging prompts for issues you catch in review help you track down the root cause faster.

Performance Review

9. Performance analysis

Analyse this code for performance issues. Identify: expensive operations that could be cached, unnecessary iterations or nested loops, N+1 query patterns, blocking operations, and memory inefficiencies. Suggest specific improvements.

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10. Database query review

Review these database queries for performance. Check for: missing indexes, N+1 patterns, unnecessary data fetching, missing query limits, and any queries that will degrade at scale.

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11. Memory usage review

Review this code for memory issues. Look for: memory leaks, large in-memory data structures that could be streamed, unnecessary object creation in hot paths, and anything that could cause issues under load.

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Readability and Maintainability

12. Naming and clarity review

Review the naming in this code — variables, functions, classes, and modules. Is everything named clearly enough that someone new could understand the intent? Suggest specific renames for anything that's ambiguous or misleading.

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13. Complexity and simplification

Where is this code more complex than it needs to be? Identify over-engineering, unnecessary abstractions, convoluted logic that could be simplified, and any "clever" code that should be made boring instead.

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14. Maintainability review

Review this code from the perspective of long-term maintenance. Is it readable to a new developer? Is the logic well-encapsulated? Are dependencies clear? What would make this harder to change in 6 months?

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15. Find code smells

Identify code smells in this code: long functions doing too many things, deep nesting, repeated logic, magic numbers, poor separation of concerns, and anything that violates single responsibility. Don't fix them — just name them and explain why they're a problem.

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Architecture and Design

16. API design review

Review this API design. Evaluate: naming conventions, RESTful principles (if applicable), error handling approach, response structure consistency, versioning considerations, and developer experience. Suggest specific improvements.

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17. Function design review

Review the design of this function. Does it do one thing? Is the interface clean? Are the parameters sensible? Should it be split up? Is the return type appropriate?

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18. Error handling review

Review the error handling in this code. Are all error cases handled? Are errors propagated correctly? Are error messages useful for debugging? Is there anything that fails silently or swallows errors?

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19. Dependency and coupling review

Review the dependencies and coupling in this code. Are there tight couplings that would make testing or replacing components difficult? Are there any circular dependencies? Could the architecture be improved for testability?

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Testing

20. Test quality review

Review these tests. Do they actually test the right things? Are there edge cases missing? Are the tests too brittle (testing implementation details instead of behaviour)? Are the assertions meaningful?

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21. Find gaps in test coverage

Here's the production code and the tests for it. What's not covered by the tests? What edge cases, error paths, or scenarios are missing? List them specifically.

Code: {{clipboard}}

22. Review test setup and teardown

Review the test setup and teardown in this file. Are tests isolated from each other? Are mocks set up correctly? Could any tests interfere with others? Is the test data setup realistic?

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Pull Request Review

23. PR diff review

I'm reviewing this pull request. Go through the diff and check for: (1) logic correctness, (2) edge cases not handled, (3) test coverage gaps, (4) anything that could break existing behaviour, (5) naming or style issues. Be specific about file and line references where possible.

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24. Review a PR description

Review this PR description. Is it clear what problem is being solved? Is the approach explained? Are there testing instructions? Are there any risks or edge cases the reviewer should know about?

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25. Summarise a PR for non-technical stakeholders

Summarise this pull request in plain English for a non-technical stakeholder. What problem does it solve? What changed? Is there any risk? What should they know about it?

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Using These Prompts Effectively

A few things that consistently improve AI code reviews:

Pick the right AI for the job. Different models have different strengths for code review. We've put together tool-specific guides for ChatGPT prompts for developers and Claude prompts for developers if you want prompts tuned to a specific model.

Be specific about what you want reviewed. "Review this code" gets a broad, shallow review. "Review this code specifically for security" gets a focused, useful one. Pick the right prompt for the goal.

Include the context the AI doesn't have. Paste the relevant function, not the whole file. If the code interacts with a database schema or an external API, paste that too.

Use the {{clipboard}} pattern. Copy your code first, then run the prompt — the clipboard gets injected automatically. It's faster than copy-pasting into a prompt template manually.

Run multiple focused reviews on important code. A security review and a performance review will each catch different things. Don't try to do everything in one prompt.

If you're running code reviews through AI regularly, storing your best prompts somewhere accessible is worth it. For IDE-integrated reviews, check out our GitHub Copilot prompts guide. Promptzy keeps these as Markdown files and pastes any prompt into Copilot Chat, Claude, or ChatGPT in under 2 seconds — no hunting for the right template mid-review. The {{clipboard}} token handles the code injection automatically.