Modern AI models like ChatGPT, Claude, and Gemini aren’t just coding assistants they’re multi-purpose problem solvers, idea expanders, and debugging partners.
Whether you’re building features, automating documentation, or tackling legacy code, these prompts go beyond the basics and into territory where LLMs really shine.
1. Refactor for Readability
textRefactor this function to improve readability and modularity without changing the logic. Use clear naming conventions and add concise comments.
Useful for cleaning up legacy code or preparing it for hand-off.
2. Generate API Documentation
textGenerate detailed API documentation from this code, including parameter descriptions, expected data types, and sample usage examples.
Helps keep documentation fresh and in sync with code updates.
3. Unit Test Generator
textWrite comprehensive unit tests for this function. Use mocks or stubs where external dependencies exist.
Works well across languages like JavaScript, Python, Go, or Java.
4. Translate Code to Another Language
textConvert this code from Python to JavaScript. Keep the logic identical and explain any key differences between language features.
Great for full-stack devs switching between environments.
5. Reverse Engineer Logic
textExplain in plain English what this block of code does, line by line. Include edge cases or assumptions if present.
Perfect when dealing with unclear third-party or legacy code.
6. Auto-generate Regex
textCreate a regex pattern that matches a US phone number format (xxx-xxx-xxxx), then explain how it works.
Saves time when working with complex validation logic.
7. Security Review Prompt
textReview this code for common security vulnerabilities (e.g. SQL injection, XSS, insecure dependencies) and suggest fixes.
Applicable in both backend and frontend contexts.
8. Performance Optimization Suggestions
textAnalyze this code for performance bottlenecks and propose alternative approaches to improve runtime efficiency.
Helpful when optimizing compute-heavy routines.
9. Convert Pseudocode to Executable Code
textTranslate this pseudocode into working code using a generic programming language of your choice. Optimize for clarity and maintainability.
Great when prototyping or planning new features.
10. Explain Error Message
textThis is the error message I got: [paste error here]. What does it mean, and how can I fix it?
Especially handy with confusing stack traces or runtime errors.
11. Data Structure Comparison
textCompare and contrast hash maps, binary search trees, and arrays. When should each be used?
Useful for interview prep or system design work.
12. Code Migration Help
textThis project is being migrated from Express.js to Fastify. Identify key changes needed and possible breaking issues.
Adjustable for any tech-to-tech migration scenario.
13. CLI Tool Mock Generator
textCreate a mock CLI tool for managing local JSON files (add, edit, delete). Code in a POSIX-compliant shell script.
Saves time when building dev utilities or scaffolding projects.
14. Comment-Only Explanation
textAdd meaningful comments to this code without changing its structure or adding extra lines.
Perfect for internal reviews and junior developer onboarding.
These prompts work well on any AI model with strong code understanding, including ChatGPT, Claude, Gemini, and open-source LLMs. You can drop them into your workflow, tweak as needed, or build your own prompt library around these patterns.
Key Takeaways
- Use specific prompts for code refactoring, API documentation, and unit test generation
- AI models excel at translating between programming languages and explaining complex code
- Security review prompts help identify vulnerabilities like SQL injection and XSS
- Combine multiple prompts for comprehensive code optimization and debugging workflows
- These language-agnostic prompts work across ChatGPT, Claude, Gemini, and open-source LLMs