AI-Powered Code Review Prompts

Code review is one of the highest-impact activities in software engineering. A good review catches bugs before they reach users, prevents technical debt from accumulating, and spreads knowledge across the team. But in practice, code review is also time-consuming, inconsistent, and constrained by reviewer availability.

AI-powered code review cannot fully replace human reviewers, but it can serve as a powerful first-pass filter. With the right prompts, AI catches subtle bugs, enforces style conventions, and identifies security issues before a human ever sees the code. This guide shows you exactly how to set up an AI code review workflow that actually works.

The Full Code Review Prompt

This is the most comprehensive prompt in our library. Use it when you want a complete review of a PR or a significant code change.

Act as a senior [LANGUAGE] engineer conducting a code review. Assume production-quality standards.

CODE UNDER REVIEW:
[PASTE FULL CODE / DIFF]

CONTEXT:
- Project type: [web app / API / CLI tool / data pipeline]
- Tech stack: [frameworks, libraries, runtime]
- Known constraints: [performance requirements / security level / backwards compatibility]

Please review this code across these dimensions:

1. BUGS & CORRECTNESS
   - Any logic errors or edge cases not handled
   - Off-by-one errors, null references, race conditions
   - Incorrect error handling or resource cleanup

2. PERFORMANCE
   - Algorithmic complexity concerns (O(n^2) or worse)
   - Unnecessary database queries or API calls
   - Memory leaks or excessive memory usage
   - Any blocking operations in async code

3. READABILITY & MAINTAINABILITY
   - Naming that is unclear or misleading
   - Functions that are too long or do too much
   - Missing comments where the intent is unclear
   - Code duplication that should be extracted

4. SECURITY
   - Input validation gaps
   - SQL injection, XSS, or injection risks
   - Hardcoded secrets or credentials
   - Sensitive data in logs or error messages

5. TESTING
   - Missing unit tests for new logic
   - Edge cases not covered by tests
   - Flaky tests or tests that do not actually verify behavior

For each issue found, provide:
- Severity: Critical / High / Medium / Low / Nitpick
- Location: line number or function name
- Explanation: why this is a problem
- Suggested fix: corrected code snippet or approach

After the review, provide a one-sentence overall assessment:
- "Approve" if no significant issues
- "Approve with minor changes" if only low-severity issues
- "Request changes" if any high or critical issues

Focused Review Prompts

Sometimes you do not need a full review. You need the AI to focus on a specific dimension. Here are single-focus prompts:

Security-Focused Review

Review this [LANGUAGE] code from a security perspective. Assume it will run in production facing untrusted user input. Identify all potential vulnerabilities including injection attacks, authentication bypasses, data exposure, and insecure dependencies. Rate each finding from Critical to Informational and suggest specific remediations.

Performance-Focused Review

Review this [LANGUAGE] code with a focus on performance. Identify any operations with worse than O(n) complexity, unnecessary allocations, blocking calls in async paths, or database query inefficiencies. For each issue, calculate the approximate performance impact and suggest an optimization with benchmark evidence if possible.

Beginner-Friendly Review

I am a junior developer and wrote this [LANGUAGE] code. Please review it like a patient mentor. For each issue, explain WHY it matters in terms I can understand, not just what to change. Include at least 2 things I did well to reinforce good habits.

Reviewing Pull Requests

Reviewing individual files is useful, but reviewing a full pull request requires a different prompt structure.

You are reviewing a pull request for a [LANGUAGE] project. Here is the diff:

[DIFF]

And the PR description:
[PR DESCRIPTION]

Please provide:
1. A summary of what this PR does (2-3 sentences)
2. The main architectural concern, if any
3. The highest-risk change and why
4. Whether the PR description accurately reflects the changes
5. Whether the change is appropriately sized (suggest splitting if too large)
6. Any missing context a reviewer needs (e.g., related PRs, migration steps)
7. Your overall recommendation: Approve / Approve with changes / Request changes / Needs discussion

Automating Code Review with Git Hooks

For teams that want to integrate AI review into their workflow, the most practical approach is a pre-commit or pre-push hook that runs the review prompt against staged changes.

A basic workflow looks like this:

1. Developer commits code as normal
2. A hook extracts the diff of the commit
3. The diff is passed to an AI API along with a focused review prompt
4. Critical findings block the commit and display inline
5. Medium/low findings are logged as suggestions

Because this is client-side, it does not replace CI/CD checks. It serves as an early warning system at commit time.

What AI CodeReview Cannot Do

It is important to understand the limitations of AI code review:

• Domain context: AI does not know your business logic. It will not catch bugs like "this checkout flow skips tax calculation" unless you explicitly describe the business rules.
• Architecture judgment: Should this feature be in Service A or Service B? The AI lacks understanding of your system's architecture.
• Understanding intent: If the code implements a requirement incorrectly, the AI only knows the code is internally consistent — not that it does the wrong thing.
• Missing tests it cannot see: If you did not paste your test files, the AI assumes tests are missing even if they exist in another file.

Building a Team Code Review Prompt Standard

If you are integrating AI review into a team workflow, customize the prompts to match your team's conventions:

• Add your coding style guide references (e.g., "Follow PEP8 with 120-character line limits")
• Include prohibited patterns specific to your codebase
• Reference your test coverage threshold
• Specify your security classification (internal, customer-facing, PII-handling)

Store these customized prompts in a shared document (or a `.promptuse.md` file in your repo) so every team member uses the same review criteria.

Next Steps

After implementing AI code review, the next optimization is to use AI for SQL query optimization and API documentation generation. Both complement a solid code review practice by catching issues before they reach review at all.