AI Prompts for Code Review

AI-assisted code review is not about replacing human reviewers — it is about catching the things humans routinely miss under time pressure. Security vulnerabilities, inconsistent error handling, missing edge cases, performance antipatterns, and deviations from project conventions are all areas where AI excels because it does not get fatigued or distracted. But "review this code" is too vague to be useful. Effective code review prompts specify the language and framework, the type of review you want (security, performance, readability, or all three), the project's conventions, and the severity level of feedback you care about. The prompts below are organized by review focus area so you can run targeted passes rather than one unfocused sweep.

Security review prompts should instruct the AI to check for the OWASP Top 10 categories relevant to your stack — SQL injection, XSS, insecure deserialization, broken authentication — and to flag any user input that reaches a database query, file system operation, or external API call without sanitization. Performance review prompts work best when you specify your scale: an N+1 query is irrelevant in a script that runs once a day but critical in a request handler serving thousands of concurrent users. Include your ORM, database, and expected load. For refactoring suggestions, provide context about the codebase's age and your team's bandwidth — the AI should suggest practical improvements, not a rewrite. Test coverage prompts should ask the AI to identify untested code paths, suggest specific test cases, and flag any logic that is difficult to test, which often indicates a design problem worth addressing.

Standardize your code review prompts across your team. When everyone uses the same review templates, you get consistent coverage and catch the same categories of issues regardless of who runs the review. PromptingBox lets you save, version, and share code review prompts so your entire engineering team benefits from the same proven templates.