Claude Prompts for Code Review: Get Better Feedback on Your Code Instantly

Code review is one of the highest-leverage activities in software development — and one of the most time-consuming. Whether you are a solo developer trying to catch your own mistakes before a pull request, a team lead reviewing junior work, or an engineer trying to enforce consistent standards across a growing codebase, the process is slow, cognitively demanding, and easy to do inconsistently. Claude is exceptionally well-suited to accelerating this process. Its ability to understand context, reason about code structure, and provide feedback at the level of a senior engineer makes it one of the most practical AI tools available for developers today. This guide gives you ready-to-use Claude prompts for every stage of code review, along with guidance on how to structure your prompts for the most useful output.

Claude stands out among AI models for code review tasks for several reasons. It has a large context window that allows it to hold entire files, multiple functions, or even whole modules in mind simultaneously — which means it can spot issues that span across functions, not just within a single block. It is trained to follow instructions precisely, so when you tell it to focus on performance and ignore style, it will. And its written output is structured and clear — it does not just flag issues, it explains why they are problems and suggests concrete fixes. When you combine Claude's reasoning depth with well-structured prompts, you get feedback that rivals a careful review from a senior engineer who has read every line of your code.

Before jumping into specific prompts, it helps to understand what makes a code review prompt effective. The best Claude prompts for code review share three characteristics: they establish a clear role (e.g. "act as a senior engineer"), they specify a focus area (e.g. "look for security vulnerabilities only"), and they set an output format (e.g. "return a numbered list of issues, each with a severity rating and a suggested fix"). When you combine all three, Claude's output becomes consistent, actionable, and easy to work with across multiple review sessions.

Use these prompts when you want a broad, comprehensive review of a block of code with no specific constraint on focus area.

"Act as a senior software engineer conducting a thorough code review. Review the following code and provide feedback on: (1) correctness and logic errors, (2) performance and efficiency, (3) readability and naming conventions, (4) error handling and edge cases, and (5) any security concerns. For each issue found, rate its severity as Critical, High, Medium, or Low, and provide a specific suggestion for how to fix it. Here is the code: [paste your code]"

"Review the following code and give me a concise bullet-point list of the three most important issues I should fix before merging this into production. Focus only on the highest-impact problems — do not mention minor style issues unless they affect readability significantly. Code: [paste your code]"

"I am building a [brief description of the project — e.g. REST API for a financial application]. The following function is responsible for [describe what it does]. Please review it with that context in mind and flag anything that could cause problems in production, especially around [your specific concern, e.g. concurrency, data integrity, API response handling]. Code: [paste your code]"

When your priority is finding bugs and logic errors rather than style or architecture issues, these targeted prompts will give you sharper, more focused output.

"Carefully read the following code and identify any logic errors, off-by-one errors, incorrect conditionals, or edge cases that could produce unexpected results. For each issue, explain what input or condition would trigger the bug and what the incorrect behaviour would be. Code: [paste your code]"

"Review the following code and focus specifically on how it handles null values, empty inputs, unexpected data types, and boundary conditions. List every scenario where the code could fail or behave unexpectedly, and for each one, suggest a defensive fix. Code: [paste your code]"

"I am reviewing a pull request. Below is the git diff for the changes. Please review only the changed lines (additions and modifications) and flag any bugs, regressions, or issues that the change could introduce. Do not comment on unchanged code. Diff: [paste your git diff]"

Security vulnerabilities are one of the most valuable things Claude can surface during code review, especially for developers who are not security specialists. These prompts direct Claude's attention to the most common and critical security concerns.

"Act as a security-focused code reviewer. Review the following code for common security vulnerabilities including but not limited to: SQL injection, XSS, insecure direct object references, improper input validation, hardcoded credentials, insecure dependencies, and improper error handling that exposes sensitive information. For each vulnerability found, explain the attack vector, the potential impact, and how to remediate it. Code: [paste your code]"

"Review the following authentication and authorisation logic for security flaws. Focus on: whether tokens are properly validated, whether session management is secure, whether privilege escalation is possible, and whether unauthorised access could be achieved through manipulation of input parameters or headers. Code: [paste your code]"

Use these prompts when you need to identify performance bottlenecks, inefficient algorithms, or database query problems.

"Analyse the following code for performance issues. For each function or loop, identify the time and space complexity. Flag any O(n²) or worse operations that could be optimised, any unnecessary re-computation that could be cached, any database queries inside loops (N+1 problems), and any memory leaks or large object allocations that could be reduced. Suggest a more efficient alternative for each issue. Code: [paste your code]"

"Review the following database queries and ORM code for performance problems. Focus on: missing indexes, full table scans, inefficient joins, N+1 query patterns, missing pagination on large result sets, and any queries that could be replaced with a single more efficient query. Explain the performance impact of each issue and suggest a rewrite. Code: [paste your code]"

When your goal is improving the long-term maintainability and clarity of code rather than fixing bugs, these prompts are your starting point.

"Review the following code purely for readability and naming quality. Flag any variable, function, or class names that are unclear, misleading, or inconsistent with common conventions for this language. Suggest better names for each and explain briefly why the original name was problematic. Do not comment on logic or performance. Code: [paste your code]"

"Review the following code and identify opportunities for refactoring that would improve maintainability without changing behaviour. Look for: repeated code that should be extracted into a function, overly long functions that should be split, deeply nested logic that could be flattened, magic numbers or strings that should be named constants, and any SOLID principle violations. For each suggestion, show a brief before/after example. Code: [paste your code]"

"Review the following code and identify the most important test cases that are missing. For each gap, describe the scenario that should be tested, why it is important to cover, and write a brief example test case in [your testing framework, e.g. Jest, pytest, RSpec]. Focus on edge cases, error paths, and any logic branches that are not yet covered. Code: [paste your code]"

Always specify the language and any relevant framework at the start of your prompt. "This is Python 3.11 using FastAPI" or "This is TypeScript with React 18" gives Claude the context it needs to apply the right idioms, best practices, and framework-specific patterns to its feedback. Generic reviews without this context will sometimes produce suggestions that are correct in the abstract but wrong for your specific stack.

If you already know about certain limitations in your code — legacy constraints, intentional trade-offs, or known issues you are not fixing in this change — tell Claude upfront. This prevents it from spending its output on issues you are already aware of and focuses its attention on things you actually need to discover.

One of Claude's most valuable capabilities in code review is not just identifying problems but rewriting the problematic section with the fix applied. Adding "and rewrite the problematic section with your suggested fix applied" to any of the prompts above will give you both the diagnosis and the corrected code in a single response, which is significantly faster than implementing feedback manually.

For code that handles critical functionality — authentication, payments, data migrations, public APIs — running the same review prompt through multiple models simultaneously and comparing their findings is a powerful way to increase coverage. Chat Smith makes this straightforward: you can run your code review prompt through Claude, GPT-4o, and Gemini in parallel and see all three responses side by side. Each model has different training emphases and will sometimes catch different classes of issues — using all three as a reviewing committee gives you significantly broader coverage than any single model alone.

You can make any of the prompts above more precise by adding language-specific instructions. Here are example additions for common languages.

Add to any prompt: "This is Python 3.11. Flag any non-Pythonic patterns, improper use of list comprehensions vs generator expressions, incorrect use of mutable default arguments, missing type hints, and any violations of PEP 8 that affect readability rather than just style."

Add to any prompt: "This is TypeScript 5 in a Next.js 14 application. Flag: implicit any types, missing null checks, unhandled promise rejections, synchronous operations that should be async, and any React hook dependency array issues if applicable."

Add to any prompt: "This is PostgreSQL 15. Flag: missing WHERE clauses on UPDATE or DELETE statements, non-SARGable predicates that prevent index use, implicit type conversions in join conditions, and any queries that would perform poorly at scale (>1 million rows)."

All of the prompts in this guide work directly in Claude's interface. If you want to go further — running your code review prompt through Claude, GPT-4o, and Gemini simultaneously and comparing which model catches what — Chat Smith is the fastest way to do it. A single prompt, all the leading models, side-by-side output. For any code that matters, the multi-model approach is the most thorough review you can run without a human reviewer in the room.

1. Can Claude review an entire file or just a function?

Claude can review an entire file, multiple functions, or even multiple files if they fit within its context window. For best results with large files, focus Claude on the section most relevant to your review goal rather than asking it to review everything at once — you will get more precise and actionable feedback.

2. How specific should my prompt be for code review?

The more specific, the better. Telling Claude "review this code" will produce generic output. Telling Claude "act as a senior Python engineer, review this function for security vulnerabilities in its input validation, and return a numbered list of issues with severity ratings" will produce output you can act on immediately. Specificity in role, focus area, and output format are the three levers that most improve Claude's code review quality.

3. Can Claude suggest fixes, not just flag issues?

Yes — and it does this well. Asking Claude to "rewrite the problematic section with your fix applied" alongside its review prompt produces corrected code that you can review, compare against the original, and implement directly. This is one of Claude's most practically useful code review capabilities.

4. Which AI model is best for code review?

Claude is widely regarded as one of the strongest models for code review, particularly for instruction following, explanation quality, and large context handling. GPT-4o is also strong, especially for code generation. For critical code, running the same prompt through both on Chat Smith and comparing outputs gives you broader coverage than either model alone.