Code review isn’t just about catching bugs , it’s about shaping code into something cleaner, safer, and faster.
If you’ve ever worked in a team, you know how valuable a fresh set of eyes can be. But manual reviews can be inconsistent. Some reviewers focus on syntax, others on logic, and security often slips through the cracks.
That’s where prompt-based code reviews come in.
By asking the right questions consistently, every time - you can make sure every pull request goes through the same rigorous evaluation.
In this article, we’re building a Prompt Library for Code Review with 30 prompts across three crucial areas:
- Readability – because code should be understood as easily as it runs.
- Security – because even a single vulnerability can sink an application.
- Performance – because slow code is expensive code.
Why Prompt-Based Code Review Matters
A prompt library turns “I’ll just skim this” into “I’ll systematically evaluate this.”
Here’s why it works:
- Consistency: Every reviewer uses the same checklist, reducing variance.
- Speed: Prompts make reviews faster without missing key points.
- Training: Junior developers learn review best practices quickly.
- AI Integration: Prompts can be used directly in ChatGPT, GitHub Copilot, or internal tools to automate review suggestions.
How This Prompt Library Is Structured
We’ll break the prompts into three main categories, each with 10 targeted prompts:
- Readability Prompts – Ensures the code is clean, maintainable, and self-explanatory.
- Security Prompts – Checks for vulnerabilities, unsafe practices, and compliance.
- Performance Prompts – Identifies potential optimizations and scalability issues.
Part 1: Readability Prompts
Readable code isn’t about “pretty formatting” it’s about reducing cognitive load for future maintainers.
Prompt 1:
"Are function and variable names descriptive and consistent with project conventions?"
💡 Why: Clear naming reduces onboarding time for new developers.
Prompt 2:
"Does the code follow a consistent indentation and formatting style?"
💡 Why: Formatting consistency improves scanability.
Prompt 3:
"Are there unnecessary comments that repeat what the code already says?"
💡 Why: Redundant comments create clutter and often get outdated.
Prompt 4:
"Where comments exist, do they explain the why rather than the what?"
💡 Why: Code explains “what,” but comments should clarify “why.”
Prompt 5:
"Is the function size small enough to be easily understood in one read?"
💡 Why: Smaller functions improve testability and comprehension.
Prompt 6:
"Does the code avoid deeply nested conditionals and loops where possible?"
💡 Why: Deep nesting reduces readability and increases complexity.
Prompt 7:
"Are magic numbers replaced with named constants or configuration values?"
💡 Why: Magic numbers confuse maintainers; constants explain meaning.
Prompt 8:
"Does the code follow the project’s naming convention for classes, methods, and variables?"
💡 Why: Naming consistency prevents confusion.
Prompt 9:
"Is the code free of dead/unreachable code blocks?"
💡 Why: Dead code wastes mental bandwidth.
Prompt 10:
"Would a new developer be able to understand this file without asking for explanations?"
💡 Why: The ultimate readability test.
Part 2: Security Prompts
Security should never be an afterthought. These prompts help reviewers spot vulnerabilities before they reach production.
Prompt 11:
"Does the code handle all user inputs with proper validation and sanitization?"
💡 Why: Prevents injection attacks and malformed input errors.
Prompt 12:
"Are sensitive data (passwords, API keys, tokens) kept out of the codebase?"
💡 Why: Secrets in code can be catastrophic if leaked.
Prompt 13:
"Is the code using parameterized queries or ORM safeguards against SQL injection?"
💡 Why: Prevents one of the most common and dangerous vulnerabilities.
Prompt 14:
"Are API responses sanitized to prevent data leaks?"
💡 Why: Prevents exposing unnecessary internal data.
Prompt 15:
"Is authentication and authorization handled consistently across endpoints?"
💡 Why: Prevents privilege escalation and unauthorized access.
Prompt 16:
"Is session management secure (timeouts, cookie flags, CSRF tokens)?"
💡 Why: Prevents session hijacking and CSRF attacks.
Prompt 17:
"Are file uploads validated for type, size, and content before processing?"
💡 Why: Prevents malicious uploads and server compromise.
Prompt 18:
"Are external dependencies verified for security vulnerabilities?"
💡 Why: Third-party libraries can introduce hidden risks.
Prompt 19:
"Is error handling avoiding leakage of stack traces or sensitive info?"
💡 Why: Detailed errors can give attackers useful clues.
Prompt 20:
"Is data encrypted in transit (HTTPS) and at rest where applicable?"
💡 Why: Encryption protects confidentiality and integrity.
Part 3: Performance Prompts
Performance issues creep in slowly and cost you more with every user. These prompts catch them early.
Prompt 21:
"Are loops and iterations optimized to avoid unnecessary computations?"
💡 Why: Excess iterations waste CPU cycles.
Prompt 22:
"Are database queries minimized and optimized (indexes, joins, batching)?"
💡 Why: Database inefficiency is a major bottleneck.
Prompt 23:
"Is caching used where possible to reduce repeated expensive operations?"
💡 Why: Caching speeds up performance significantly.
Prompt 24:
"Is there any evidence of memory leaks or unclosed resources?"
💡 Why: Memory leaks can crash long-running processes.
Prompt 25:
"Are algorithms used the most efficient ones for the dataset size?"
💡 Why: Algorithm choice drastically affects scalability.
Prompt 26:
"Is lazy loading used for large datasets or media files?"
💡 Why: Lazy loading improves initial load times.
Prompt 27:
"Are network requests batched or minimized where possible?"
💡 Why: Fewer requests = faster load and lower bandwidth.
Prompt 28:
"Are background jobs and asynchronous processing used for heavy tasks?"
💡 Why: Prevents blocking the main thread.
Prompt 29:
"Is pagination implemented for large datasets instead of loading everything at once?"
💡 Why: Pagination prevents excessive memory and rendering time.
Prompt 30:
"Has the code been tested with realistic load or stress scenarios?"
💡 Why: Lab performance isn’t the same as real-world performance.
Best Practices for Using a Prompt Library
- Keep prompts visible during reviews (PR templates, checklists).
- Assign responsibility each reviewer covers all categories.
- Rotate reviewers to get fresh perspectives.
- Update prompts based on project evolution.
- Use AI tools (ChatGPT, CodeQL, SonarQube) with these prompts for automation.
Integrating AI & Prompt Libraries in Your Workflow
You can integrate these prompts into:
- GitHub PR templates (pre-populate checklist)
- CI/CD pipelines (run automated checks)
- AI review bots (generate recommendations from prompts)
- Code review training sessions (teach new hires best practices)
Conclusion
A good code review doesn’t just find mistakes it prevents them from happening in the first place.
This Prompt Library ensures that every review covers readability, security, and performance, making your codebase cleaner, safer, and faster over time.
Start with these 30 prompts, adapt them to your team, and watch the quality of your code and your developer happiness-skyrocket.
