Introduction
If you're a developer using Cursor—the AI-powered IDE built on VS Code—you already know it can autocomplete code, answer questions via chat, and execute commands. But the real magic happens when you craft precise prompts. In this guide, I share 10 ready-to-use prompts for Cursor's autocomplete, chat, and command modes. Each prompt is specific, copy-paste ready, with an explanation and a usage example. By the end, you'll have a cheat sheet to supercharge your daily workflow.
Why Prompts Matter in Cursor
Cursor's AI (powered by models like GPT-4 and Claude) interprets your intent. A vague prompt like "fix this" yields mediocre results. A structured prompt with context, constraints, and examples produces production-ready code. According to Cursor's official documentation, the AI works best when you provide:
- The goal (what you want to achieve)
- The context (relevant code, files, or errors)
- The format (e.g., return only the changed function)
10 Prompts for Cursor: Autocomplete, Chat, and Command Mode
1. Autocomplete: Generate a Function from a Comment
Task: Write a complete function based on a high-level comment.
Prompt to type as a comment:
// function to validate email format, return true if valid, false otherwise, handle edge cases
Explanation: Cursor's autocomplete will suggest the full function. Be descriptive but keep it in one line.
Usage example: After typing the comment, press Tab to accept the suggestion. The AI will generate a robust function with regex and edge cases.
2. Chat: Refactor a Specific Function
Task: Ask the AI to improve a selected code block.
Prompt in chat:
Refactor the selected function to use async/await, add error handling, and return a standardized response object. Keep the same external interface.
Explanation: Select the function before opening chat (Cmd+K). The prompt specifies three clear requirements.
Usage example: If you have a callback-based function, the AI will rewrite it with async/await and try-catch.
3. Command Mode: Generate a Unit Test
Task: Create a test file for a given module.
Command:
Generate Jest unit tests for the selected module. Cover all exported functions, edge cases, and mock external dependencies. Output in a new file.
Explanation: Command mode (Cmd+Shift+K) executes multi-step tasks. The prompt sets the testing framework and scope.
Usage example: The AI creates a module.test.ts file with descriptive test names and mock setup.
4. Autocomplete: Fix a Bug with a Hint
Task: Provide a hint to fix a known issue.
Prompt as a comment above the buggy line:
// This causes a race condition. Fix by adding a lock or using atomic operations.
Explanation: Cursor reads the surrounding code and suggests a fix aligned with the hint.
Usage example: For a shared counter increment, the AI might suggest AtomicInteger.incrementAndGet().
5. Chat: Explain Code in Natural Language
Task: Understand a complex piece of code.
Prompt in chat:
Explain the selected code block line by line, focusing on the algorithm and data flow. Use simple language suitable for a junior developer.
Explanation: The prompt sets the audience and depth.
Usage example: The AI will output a numbered list explaining each step.
6. Command Mode: Add Logging to All Functions
Task: Instrument code for debugging.
Command:
Add structured logging (using console.log with a timestamp and function name) to every function in the selected file. Do not change the logic.
Explanation: Command mode can batch-edit multiple functions.
Usage example: Each function gets a console.log('[2026-07-13T10:00:00Z] processData called').
7. Autocomplete: Generate a Config File
Task: Create a configuration file from a description.
Prompt as a new file comment:
// docker-compose.yml for a Node.js app with PostgreSQL and Redis, expose port 3000, use volumes for data persistence
Explanation: Cursor will generate the entire YAML file.
Usage example: The AI produces a ready-to-use Docker Compose configuration.
8. Chat: Suggest Performance Improvements
Task: Optimize a selected code block.
Prompt in chat:
Analyze the selected code for performance bottlenecks. Suggest specific improvements with code examples. Focus on reducing time complexity and memory usage.
Explanation: The prompt asks for analysis plus concrete code.
Usage example: The AI might recommend replacing nested loops with a hash map.
9. Command Mode: Add TypeScript Types
Task: Convert untyped JavaScript to TypeScript.
Command:
Add TypeScript types to all variables, function parameters, and return values in the selected file. Use interfaces for objects. Do not change runtime behavior.
Explanation: Command mode can process entire files.
Usage example: let x = 5 becomes let x: number = 5.
10. Chat: Generate Documentation Comments
Task: Add JSDoc to a selected function.
Prompt in chat:
Add JSDoc comments to the selected function. Include @param, @returns, @throws, and a brief description. Use the existing code to infer types.
Explanation: The prompt specifies the documentation format.
Usage example: The AI outputs a multi-line comment above the function.
Real-World Case: Refactoring a Legacy API Handler
Problem: A team at a mid-size SaaS company maintained a Node.js Express endpoint that handled user registration. The code was 200 lines, mixed business logic with HTTP concerns, and lacked error handling. The team needed to refactor it quickly before a compliance audit.
Solution: Using Cursor, the lead developer selected the entire route handler and used the chat prompt: "Refactor this Express route to use a controller-service-repository pattern. Extract database calls to a separate repository file, validation to a middleware, and error handling to a global handler. Do not change the external API." The AI generated three new files and modified the route file in under a minute.
Results:
- The codebase became modular and testable.
- The refactoring saved the team approximately 4 hours of manual work.
- The audit passed without issues.
Lessons Learned:
- Always specify the target architecture (e.g., controller-service-repository).
- Ask the AI to keep the external API unchanged to avoid breaking clients.
- Review the AI's output for security issues (e.g., missing input sanitization).
Best Practices for Prompting in Cursor
| Aspect | Best Practice | Example |
|---|---|---|
| Context | Provide relevant code or error messages | Select the buggy function before asking for a fix |
| Specificity | State the desired outcome exactly | "Return an array of objects with id and name" not "give me the data" |
| Constraints | Set limits on output | "Only modify the function body, not the signature" |
| Format | Specify output format | "Output as a JSON object" or "Write the result to a new file" |
| Iteration | Refine the prompt if results are off | "Use async/await instead of .then()" after first attempt |
Conclusion
Cursor's AI is a powerful assistant, but its effectiveness hinges on the quality of your prompts. Use the 10 prompts above as a starting point, then customize them to your project's tech stack and conventions. Remember to always review AI-generated code for correctness and security. With practice, you'll develop an intuition for what makes a prompt work. Happy coding!
ASI Biont supports integration with AI-assisted development tools and offers courses on effective prompting — learn more at asibiont.com/courses.
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