12 AI Prompts for Code Review That Catch Real Bugs

AI caught a SQL injection vulnerability in my code last week that I'd stared at for 20 minutes without seeing. That experience changed how I think about AI prompts and code review forever. The right prompt turns a general-purpose language model into a specialist reviewer that spots security holes, performance bottlenecks, logic errors, and quality issues with uncomfortable accuracy.

I've been refining these AI prompts for code review across dozens of production projects. Each one targets a specific class of bug. They're structured using principles from the RACE Framework, giving the AI a clear Role, Action, Context, and Expectation, so you get focused, actionable findings instead of vague suggestions.

Below are 12 prompts organized into four groups: Security (1-3), Performance (4-6), Logic (7-9), and Quality (10-12). Each prompt is copy-paste ready, followed by what it catches and a real example finding.

Table of Contents

• Why AI-Powered Code Review Works
• How to Use These Prompts
• Security Review Prompts (1-3)
• Performance Review Prompts (4-6)
• Logic Review Prompts (7-9)
• Code Quality Review Prompts (10-12)
• Combining Prompts Into a Review Workflow
• Framework Integration Tips

Why AI-Powered Code Review Works

Manual code review is essential, but humans have predictable blind spots. We skim familiar patterns, assume our own logic is sound, and rush through boilerplate. AI reviewers don't have those biases. They read every line with the same attention, and they've been trained on millions of examples of buggy code alongside their fixes.

The catch is that a generic "review this code" prompt produces generic output. You'll get surface-level comments about variable naming while a race condition lurks three functions deep. Targeted prompts fix this by directing the AI's attention to specific vulnerability classes, the same way a security auditor uses checklists rather than just "looking at stuff."

These prompts work with any major AI assistant. If you're deciding between tools, the AI coding assistants comparison covers which ones handle code review best. The key insight: the prompt matters more than the model for review tasks.

How to Use These Prompts

Each prompt follows a consistent structure inspired by the TAG Framework: Task, Action, Goal:

• Paste the prompt into your AI assistant (Claude, ChatGPT, Copilot Chat, or Cursor)
• Attach or paste your code: a file, a diff, or a pull request
• Review findings: the AI will return specific line numbers, explanations, and fix suggestions

For best results, feed the AI one file or module at a time rather than an entire codebase. Context windows are large, but focused input produces sharper output. If you're new to AI-assisted development, Getting Started with Claude Code covers the fundamentals.

A few tips before we start:

• Run multiple prompts per review. A security prompt won't flag performance issues and vice versa. Use at least one from each category for thorough coverage.
• Trust but verify. AI reviewers produce false positives. Treat findings as leads, not verdicts.
• Iterate on context. If a prompt misses something, add more context about your architecture, dependencies, or threat model.

Security Review Prompts (1-3)

Security bugs are the highest-stakes findings in any code review. A single missed injection vulnerability or broken authentication check can compromise an entire system. These three prompts cover injection attacks, authentication and authorization flaws, and data exposure risks.

Prompt 1: Injection Vulnerability Scanner

What it catches: Any path where untrusted input reaches a dangerous sink (database queries, shell commands, HTML output, template engines, or HTTP headers) without proper sanitization or parameterization.

Example finding: In a Node.js Express handler, the prompt flagged this line as Critical SQL injection:

The req.body.email value flows directly into the query string. An attacker can submit ' OR 1=1 -- as the email to dump the entire users table. The fix uses a parameterized query.

Prompt 2: Authentication and Authorization Auditor

What it catches: Missing or bypassable access controls, insecure credential storage, broken session handling, and privilege escalation paths. These are OWASP Top 10 staples that slip through manual reviews because the "happy path" works fine.

Example finding: In a REST API, the prompt identified a horizontal privilege escalation:

The endpoint checks that you're logged in but never verifies you're requesting your own profile. Any authenticated user can read any other user's data by changing the ID in the URL.

Prompt 3: Sensitive Data Exposure Detector

What it catches: Leaked secrets, logged PII, missing encryption, insecure headers, and error messages that reveal internal architecture. These findings rarely break tests but can trigger data breach notifications.

Example finding: The prompt flagged sensitive data leaking through error responses:

Performance Review Prompts (4-6)

Performance bugs rarely crash your application. They degrade it slowly: an extra database round trip here, an O(n^2) loop there, until response times become unacceptable under load. These prompts target the three most common culprits.

Prompt 4: Database Query Optimizer

What it catches: N+1 queries, missing indexes, unbounded SELECTs, wasteful column fetching, long-held transactions, and ORM anti-patterns that work fine in development but collapse under production load.

Example finding: Classic N+1 in a Django view:

With 500 orders, the vulnerable version fires 501 queries. The fix uses select_related to JOIN the customer table in a single query.

Prompt 5: Memory and Resource Leak Finder

What it catches: File handles left open on error paths, event listeners that accumulate, unbounded in-memory caches, connections not returned to pools, and stream consumers that don't handle backpressure.

Example finding: Resource leak on exception path in Python:

Prompt 6: Algorithmic Complexity Reviewer

What it catches: Hidden quadratic loops, repeated linear scans that should use hash maps, wasteful sorting, string building anti-patterns, and data structure mismatches.

Example finding: Quadratic duplicate detection in JavaScript:

With 10,000 items, the original version performs up to 50 million comparisons. The Set-based version handles it in a single pass.

Logic Review Prompts (7-9)

Logic bugs are the trickiest class. The code compiles, tests pass in happy-path scenarios, and the application runs. But edge cases (null values, empty arrays, concurrent access, boundary conditions) expose flawed assumptions. These prompts target the patterns that cause production incidents.

Prompt 7: Edge Case and Boundary Condition Analyzer

What it catches: Off-by-one errors, null pointer dereferences, empty collection crashes, numeric overflow, floating-point comparison bugs, and date/time edge cases around DST transitions and leap years.

Example finding: Floating-point comparison bug in a pricing engine:

A 100% discount on a $0.30 item might produce 0.00000000000000004 instead of 0, causing the free check to fail.

Prompt 8: Concurrency and Race Condition Detector

What it catches: Unprotected shared state, TOCTOU bugs, deadlocks, missing awaits, database race conditions on read-modify-write sequences, and file system races.

Example finding: TOCTOU race in an inventory system:

Two concurrent purchases could both pass the stock check, overselling inventory. The fix uses an atomic database operation that checks and updates in a single statement.

Prompt 9: Error Handling and Failure Mode Reviewer

What it catches: Swallowed exceptions, overly broad catches, missing error handling on I/O, inconsistent error patterns, and cleanup code that masks original errors.

Example finding: Silent error swallowing in a payment processor:

The original code marks every order as PAID regardless of whether the charge succeeded. Failed charges are silently ignored.

Code Quality Review Prompts (10-12)

Quality issues don't cause bugs directly, but they make bugs inevitable over time. Unclear names obscure intent, missing types allow type confusion, and poor structure makes changes risky. These prompts catch the quality problems that compound into tech debt.

Prompt 10: Naming and Readability Reviewer

What it catches: Misleading names, god functions, deep nesting, magic values, useless comments, and boolean trap parameters. The ROSES Framework (Role, Objective, Scenario, Expected Solution, Steps) provides another effective structure for crafting review prompts when you need more scenario context.

Example finding: Boolean trap making call sites unreadable:

Prompt 11: Type Safety and API Contract Reviewer

What it catches: Missing types, any usage that defeats TypeScript's purpose, nullable access without guards, inconsistent API shapes, missing input validation, and unsafe type assertions.

Example finding: Unsafe type assertion hiding a bug:

Prompt 12: Test Coverage and Testability Reviewer

What it catches: Untested error paths, hard-coded dependencies blocking mocks, loose assertions, flaky test patterns, and missing integration tests on critical flows.

Example finding: Hard-coded dependency preventing unit tests:

The original class instantiates EmailService directly, making it impossible to unit test complete_order without sending real emails. Dependency injection fixes this cleanly.

Combining Prompts Into a Review Workflow

Running all 12 prompts on every pull request isn't practical. Here's a tiered approach that balances thoroughness with speed:

Every PR (takes 5 minutes):

• Prompt 1 (Injection): security is non-negotiable
• Prompt 4 (Database): N+1 queries are the most common performance killer
• Prompt 9 (Error Handling): silent failures cause production incidents

Weekly deep review (takes 20 minutes):

• Add Prompts 2, 3 (remaining security prompts)
• Add Prompts 7, 8 (edge cases and concurrency)
• Add Prompt 11 (type safety)

Before major releases:

• Run all 12 prompts
• Feed findings into your test suite as regression tests

You can automate this by creating a shell script that pastes each prompt with your diff into an AI API. Several teams I've worked with have reduced their production incident rate by 40-60% after adopting structured AI review prompts alongside manual code review.

For more on integrating AI into your development workflow, see prompt engineering for AI coding assistants. If you're comparing which tool handles reviews best, the comparison of AI coding assistants breaks down the strengths of each option.

Framework Integration Tips

Each prompt above follows a pattern: Role, Task with specific checklist, output format requirements. This structure draws from established prompt frameworks that you can explore further:

• The RACE Framework (Role, Action, Context, Expectation) is what these prompts are built on. It gives the AI a persona, a specific task, surrounding context, and clear output expectations.
• The TAG Framework (Task, Action, Goal) works well for simpler review requests where you want a quick scan rather than a deep audit.
• The ROSES Framework (Role, Objective, Scenario, Expected Solution, Steps) is ideal when you need to describe a complex scenario the reviewer should consider.

You can customize any prompt above by adjusting the Role to match your domain (fintech, healthcare, e-commerce), adding project-specific patterns to the checklist, or tightening the output format. The structure stays the same; only the details change.

If you're building a broader AI-assisted coding workflow beyond code review, prompt engineering for AI coding assistants covers techniques for code generation, debugging, and refactoring prompts that complement these review prompts.

Quick Reference

Grab one prompt, paste your code, and see what it finds. Start with Prompt 1. You might be surprised what's been hiding in plain sight.