Writing Prompts for Code Review and Debugging

AI has become an invaluable tool for developers, capable of reviewing code, spotting bugs, and suggesting improvements. However, getting useful feedback requires knowing how to ask the right questions. This guide teaches you how to write prompts that turn AI into an effective code review partner and debugging assistant, helping you catch issues faster and write better code.

Why AI Code Review Matters

Code review is essential for maintaining quality, catching bugs, and ensuring maintainability. Traditional code review requires another developer's time and attention, which isn't always immediately available. AI-assisted code review provides instant feedback, allowing you to catch obvious issues before human review or identify problems in personal projects where formal review isn't available.

AI excels at pattern recognition, making it particularly good at spotting common mistakes, identifying potential bugs, checking for best practices, suggesting performance improvements, and finding security vulnerabilities. While AI shouldn't replace human code review entirely, it serves as an excellent first pass that catches many issues quickly.

Key Principle:

AI code review is most effective when you ask specific, focused questions rather than generic "review this code" requests. The more targeted your prompt, the more valuable the feedback.

Writing Effective Code Review Prompts

Provide Essential Context

AI needs to understand what your code does and what language or framework you're using. Don't assume it can figure everything out from the code alone. Context helps the AI provide relevant, actionable feedback.

Context Example:

Weak: "Review this code"

Strong: "Review this Python function that processes user uploads. It's part of a Flask web application. I'm concerned about error handling and edge cases."

Specify What to Focus On

Code can be reviewed from many angles—readability, performance, security, maintainability, correctness. Tell the AI which aspects matter most for your current needs. This produces focused, useful feedback rather than an overwhelming list of every possible improvement.

Focus Areas:

"Focus on potential bugs and edge cases"
"Review for security vulnerabilities"
"Check for code readability and maintainability"
"Analyze performance and efficiency"
"Ensure this follows React best practices"

Set Experience Level Expectations

Let the AI know your experience level so it can calibrate its explanations. Feedback for a beginner should be more detailed and educational, while experienced developers might prefer concise, advanced suggestions.

Experience Level Context:

"I'm relatively new to async JavaScript, so please explain any issues with promises or async/await in detail."

"I'm an experienced Python developer but new to Django—focus on Django-specific concerns."

Ask for Explanations, Not Just Fixes

Understanding why something is a problem helps you avoid similar issues in the future. Request explanations alongside suggestions for corrections.

Explanation Request:

"Review this code and for each issue you find, explain why it's a problem and what could go wrong, then suggest a fix."

Pro Tip:

Structure your code review prompts as: [Context] + [Code] + [Focus Areas] + [Specific Concerns]. This formula consistently produces high-quality feedback.

Debugging Strategies with AI

Describe the Problem Clearly

When debugging, provide more than just code. Explain what you expect to happen, what actually happens, and any error messages. The gap between expected and actual behavior helps AI identify the root cause.

Effective Debugging Prompt:

"This function should filter out duplicate items from an array and return sorted results. However, it's returning an empty array when I pass in [3, 1, 2, 1, 3]. Here's the code and the error message I'm seeing: [code and error]. What's going wrong?"

Include Relevant Context

Show the surrounding code if the bug might be caused by how the function is called or what data it receives. Sometimes the problem isn't in the function itself but in how it's being used.

Start with Specific Questions

Rather than "find the bug," ask about specific suspicious parts of your code. This directed approach often yields faster results.

Specific Debugging Questions:

"Could this null pointer exception be caused by this line?"
"Is my loop condition correct, or could it cause an infinite loop?"
"Why might this async function not be awaiting properly?"
"Could this variable be out of scope when the callback executes?"

Request Step-by-Step Analysis

For complex bugs, ask the AI to walk through the code execution step by step. This often reveals where things go wrong.

Step-by-Step Request:

"Walk through this code line by line with the input [1, 2, 3] and show me the value of each variable after each step. I think something's wrong with how I'm updating the counter."

Important:

Always verify AI suggestions by testing them. AI can identify likely problems and suggest solutions, but it doesn't execute code, so it might occasionally miss context-specific issues.

Prompts for Specific Review Types

Readability and Maintainability Review

Prompt: "Review this code for readability and maintainability. Focus on variable naming, code organization, and whether the logic is easy to follow. Suggest improvements that would make this easier for other developers to understand and modify."

What this catches: Unclear variable names, overly complex logic, poor function organization, lack of comments where needed, inconsistent formatting.

Error Handling Review

Prompt: "Review this function's error handling. What edge cases am I not handling? What could cause this to fail unexpectedly? Where should I add try-catch blocks or input validation?"

What this catches: Missing null checks, unhandled exceptions, lack of input validation, edge cases that could cause crashes, insufficient error messages.

Logic and Correctness Review

Prompt: "Check this algorithm for logical errors. Does it correctly handle all cases? Are there scenarios where it would produce wrong results? Test it mentally with edge cases like empty inputs, single items, or maximum values."

What this catches: Off-by-one errors, incorrect loop conditions, logic bugs, wrong comparison operators, issues with boundary conditions.

Best Practices Review

Prompt: "Review this [language/framework] code for adherence to best practices and common patterns. Am I using the language idiomatically? Are there standard approaches I should use instead of my current implementation?"

What this catches: Non-idiomatic code, reinventing the wheel, ignoring built-in utilities, anti-patterns, violations of language conventions.

Framework-Specific Review:

"Review this React component for React best practices. Am I using hooks correctly? Should I be using useCallback or useMemo anywhere? Are there unnecessary re-renders?"

Code Duplication Review

Prompt: "Identify any code duplication or repeated patterns in this module. Where could I extract common functionality into reusable functions? What patterns could be abstracted?"

What this catches: Repeated code blocks, similar functions that could be consolidated, opportunities for abstraction, places where DRY principle is violated.

Security and Performance Reviews

Security-Focused Prompts

Security reviews require specific attention to common vulnerabilities. Be explicit about security concerns in your prompt.

Security Review Prompts:

"Review this code for security vulnerabilities. Check for: SQL injection risks, XSS vulnerabilities, insecure data handling, authentication/authorization issues, and exposure of sensitive data."

"This function handles user input. What security risks do you see? How could a malicious user exploit this?"

"Review this API endpoint for common security issues. Is input properly validated? Are there potential injection attacks?"

Performance-Focused Prompts

When performance matters, ask specifically about efficiency, bottlenecks, and optimization opportunities.

Performance Review Prompts:

"Analyze this code for performance issues. What's the time complexity? Are there unnecessary operations? How would this scale with large datasets?"

"This function processes thousands of records and feels slow. Where are the bottlenecks? What optimizations would have the biggest impact?"

"Review this database query for performance. Are there missing indexes? Could this be done more efficiently?"

Performance Tip:

When asking about performance, mention the expected data size or load. "This processes 10 records" vs. "This processes 100,000 records" leads to very different optimization advice.

Why Context Matters in Code Prompts

Include Relevant Surrounding Code

A function doesn't exist in isolation. If understanding the problem requires seeing how data flows in or out, include that context. Show the calling code, related functions, or data structures.

Explain Constraints and Requirements

Sometimes code looks suboptimal but exists for good reasons. Explain any constraints: "We can't use external libraries," "This must be compatible with IE11," "We're optimizing for memory, not speed."

Describe the Broader System

Context about the larger system helps AI understand why certain choices were made and suggest appropriate alternatives.

System Context:

"This function runs in a serverless environment with 128MB memory and 3-second timeout. It's called thousands of times per day. Review with these constraints in mind."

Mention Previous Issues

If this code has had problems before, mention them. This helps AI focus on areas that have been problematic.

Historical Context:

"This function has been causing intermittent timeout errors in production, usually when handling large files. I've already fixed the obvious issues with memory allocation—what else could be causing timeouts?"

Common Mistakes to Avoid

Dumping Code Without Context

Pasting code with no explanation forces the AI to guess what you want reviewed and why. Always provide at least a sentence of context about the code's purpose and your concerns.

Asking Too Broadly

"Review everything about this code" produces overwhelming, unfocused feedback. Prioritize what matters most for your current needs.

Avoid This:

"Tell me everything that's wrong with this code" generates a long list of every possible improvement, from critical bugs to minor style preferences, making it hard to prioritize.

Not Providing Error Messages

When debugging, error messages are incredibly valuable. Include the full error message, stack trace, and any relevant console output. These provide crucial clues about what's going wrong.

Ignoring Language/Framework Versions

Best practices and available features vary by version. Mention if you're using an older version or have specific compatibility requirements.

Version Context:

"Review this JavaScript code. I'm using ES5 (no ES6 features) because we need IE11 support."

Not Following Up

If the AI's suggestion isn't clear or you want deeper explanation, ask follow-up questions. Code review with AI should be a conversation, not a single question and answer.

Good Follow-Ups:

"You mentioned this could cause a race condition. Can you explain how?"
"The refactoring you suggested looks good, but how would I handle the error case?"
"Is there a simpler approach that wouldn't require restructuring so much?"

Treating AI Feedback as Gospel

AI provides suggestions, not commandments. Use your judgment to evaluate whether feedback makes sense for your specific situation. Sometimes the AI suggests changes that don't fit your constraints or misunderstands the requirements.

Best Practice:

Use AI code review as one input among many. Combine it with testing, profiling tools, linters, and human review for comprehensive quality assurance.

Advanced Techniques

Comparative Review

If you have multiple approaches, ask AI to compare them: "I have two ways to implement this feature. Here's approach A and approach B. Compare them in terms of readability, performance, and maintainability."

Test Case Generation

Ask AI to suggest test cases: "What test cases should I write for this function? Include edge cases and error conditions I should verify."

Refactoring Guidance

Use AI for refactoring suggestions: "This function has grown too complex. How could I refactor it into smaller, more focused functions? Show me a possible structure."

Code Explanation

When reviewing unfamiliar code: "Explain what this code does step-by-step. Then identify any potential issues or improvements."

Effective code review and debugging with AI comes down to clear communication. The more specific and contextual your prompts, the more valuable the feedback. Start with focused questions, provide relevant context, and engage in follow-up dialogue to dig deeper into suggestions. Over time, you'll develop intuition for exactly what information AI needs to provide the most helpful code review and debugging assistance.