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Posted on • Originally published at promptzy.app

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

#ai #codereview #programming #productivity

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

The quality of AI code reviews hinges primarily on prompt design rather than the underlying model. Generic requests yield shallow feedback, whereas targeted prompts focused on specific concerns—security, performance, naming conventions—produce actionable insights that catch real issues.

These prompts function across ChatGPT, Claude, Copilot Chat, and Gemini. Rather than rewriting identical prompts for each pull request, you can save them for reuse. The author has employed similar versions for approximately one year and reports they've identified genuine bugs.

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.

{{clipboard}}
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2. Quick Scan

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.

{{clipboard}}
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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.

{{clipboard}}
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4. Junior Developer Focused Review

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.

{{clipboard}}
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Security Review

These prompts apply to code handling user input, authentication, or sensitive information.

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.

{{clipboard}}
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6. Authentication and Authorization 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?

{{clipboard}}
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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?

{{clipboard}}
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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?

{{clipboard}}
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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.

{{clipboard}}
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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.

{{clipboard}}
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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.

{{clipboard}}
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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.

{{clipboard}}
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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.

{{clipboard}}
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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?

{{clipboard}}
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15. Code Smell Identification

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.

{{clipboard}}
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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.

{{clipboard}}
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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?

{{clipboard}}
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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?

{{clipboard}}
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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?

{{clipboard}}
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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?

{{clipboard}}
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21. Test Coverage Gaps

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}}
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22. Test Setup and Teardown Review

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?

{{clipboard}}
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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.

{{clipboard}}
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24. PR Description Review

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?

{{clipboard}}
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25. PR Summary 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?

{{clipboard}}
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Using These Prompts Effectively

Pick the Right AI Tool

Different models excel at different aspects of code review. There are specialized guides for ChatGPT and Claude specifically optimized for developer workflows.

Be Specific About Review Scope

Broad requests like "review this code" produce shallow assessments. Targeted prompts addressing particular concerns — "specifically for security" — yield focused, useful feedback. Match the prompt to your review objective.

Include Necessary Context

Provide relevant code sections rather than entire files. When code interacts with database schemas or external APIs, include those details too.

Leverage the {{clipboard}} Pattern

Copy code first, then execute the prompt. The clipboard automatically injects content, accelerating the workflow compared to manual copy-paste operations.

Run Focused Multiple Reviews

Security and performance reviews identify different issues. Execute multiple specialized reviews on critical code rather than attempting comprehensive coverage in one prompt.

If you're doing regular AI-assisted code reviews, keeping these prompts accessible saves real time. Promptzy stores them as Markdown files and pastes them into Copilot Chat, Claude, or ChatGPT in under two seconds — no hunting for templates mid-review. The {{clipboard}} token handles the code injection automatically.

Originally published at promptzy.app/blog

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