Voice Dictation for Cursor: Code Comments, Documentation & AI Prompts
Enhance your AI-powered coding with voice input. Contextli helps you write code comments, documentation, and AI prompts faster.
Cursor brings AI to your code editor—and Contextli brings your voice. While Cursor helps you write code, Contextli helps you write everything around it: comments, documentation, AI prompts, and explanations. Together, they create a more natural, efficient development experience.
Contextli Modes for Cursor
Each mode transforms your voice into Cursor-ready content with specific formatting.
Code Comment
Clear comments explaining code intent
Best for: Inline code comments
You say:
"Comment explaining why we're using a mutex here instead of a read write lock, it's because our write operations are frequent and we don't want readers to starve writers, the performance trade-off is acceptable for data consistency"
Contextli outputs:
// Using mutex over RWLock: Our write operations are frequent enough that // RWLock would cause writer starvation. The slight read performance penalty // is acceptable for guaranteed data consistency.
Docstring
Function and class documentation
Best for: Function and class documentation
You say:
"Docstring for a function called process payment that takes an amount in cents and a customer ID and returns a payment result object with status and transaction ID, throws if the amount is negative or customer not found"
Contextli outputs:
/**
* Process a payment for a customer.
*
* @param {number} amountCents - Payment amount in cents (must be positive)
* @param {string} customerId - Unique customer identifier
* @returns {PaymentResult} Object containing status and transactionId
* @throws {Error} If amountCents is negative
* @throws {CustomerNotFoundError} If customerId doesn't exist
*
* @example
* const result = await processPayment(2500, 'cust_123');
* // result: { status: 'success', transactionId: 'txn_456' }
*/AI Prompt
Prompts for Cursor's AI features
Best for: Directing Cursor's AI features
You say:
"AI prompt to refactor this function to use async await instead of callbacks, preserve the error handling but make it cleaner, and add proper TypeScript types for the parameters and return value"
Contextli outputs:
Refactor to async/await: 1. Convert callback-based flow to async/await 2. Preserve all error handling logic 3. Simplify error handling using try/catch 4. Add TypeScript types: - All function parameters - Return type (Promise<T>) 5. Maintain existing functionality Focus: Clean, readable async code with proper typing.
How to Use Contextli in Cursor
Step-by-step workflows for common Cursor tasks.
Document Code with Voice
You've written complex logic and need to add comments and docstrings before committing.
- 1
Position cursor above function
Where docstring should go
- 2
Activate Docstring mode
Structured documentation
⌘+Shift+V - 3
Describe the function's purpose
Parameters, returns, throws
- 4
Repeat for key functions
Build comprehensive documentation
Example Output:
/** * Validates user input against schema rules. * @param input - Raw user input * @returns ValidationResult...
Voice to Cursor: Real Examples
What you say versus what Contextli produces for Cursor.
"This implements optimistic locking by checking the version number before updating, if another process modified the record the version won't match and we retry with exponential backoff, we chose optimistic over pessimistic locking because our reads far outnumber writes"
/** * Optimistic locking implementation: * - Checks version number before updates * - If version mismatch (concurrent modification), retries with exponential backoff * * Why optimistic locking? * - Read-heavy workload (reads >> writes) * - Pessimistic locking would bottleneck reads unnecessarily * - Occasional retry cost < constant lock overhead */
Contextli vs Other Cursor Voice Solutions
Contextli vs Cursor AI Chat
| Feature | Contextli | Cursor AI Chat |
|---|---|---|
| Voice input | ✅ Native voice | ❌ Typing only |
| Documentation | ✅ Structured output | ⚠️ AI generates |
| Your explanations | ✅ Your words | ⚠️ AI interpretation |
| Speed | ✅ Real-time dictation | ⚠️ Generate and edit |
Cursor AI generates code. Contextli lets you create documentation and comments in your own voice—faster than typing, more authentic than AI-generated.
Why Use Contextli for Cursor?
Even with AI-assisted coding, developers still type a lot of non-code content. Comments explain why. Documentation explains how. AI prompts direct the AI. Contextli handles all of this by voice, letting you focus your keyboard time on actual code.
Cursor + Contextli: Common Questions
How does Contextli complement Cursor's AI features?
Cursor's AI writes code; Contextli helps you write everything else. Use Contextli for comments, documentation, AI prompts, and explanations—the human context that makes AI-generated code maintainable.
Can I dictate AI prompts for Cursor?
Yes! The AI Prompt mode helps you create clear, specific prompts for Cursor's AI features. Dictate what you want, and Contextli formats it into an effective prompt.
Cursor Integration Details
descriptionSupported Formats
- checkJSDoc comments
- checkPython docstrings
- checkCode comments (//, #, /* */)
- checkMarkdown
- checkPlain text for AI prompts
lightbulbPro Tips
- Use Docstring mode to quickly document functions before committing
- AI Prompt mode helps you write better prompts for Cursor's AI
- Combine Contextli dictation with Cursor's AI for maximum efficiency
infoGood to Know
- arrow_rightCannot generate code via voice (use Cursor AI)
- arrow_rightCannot execute Cursor commands via voice
- arrow_rightCode formatting depends on editor settings
- arrow_rightAI responses still require review
devicesPlatform Support
Works in Cursor editor on macOS.
Related Searches
Start Using Voice Dictation in Cursor
Transform how you work in Cursor with context-aware voice dictation.
Related Integrations
VS Code
Dictate code comments, documentation, and commit messages directly in VS Code. Contextli understands code context and formats your voice output appropriately.
GitHub
Create issues, write PR descriptions, and leave detailed code review comments—all by voice. Contextli formats for GitHub's markdown conventions.
GitLab
Create issues, write merge request descriptions, and document in GitLab wiki—all by voice. Contextli understands GitLab's conventions.
Linear
Create issues, write project updates, and plan cycles—all by voice. Contextli matches Linear's streamlined aesthetic with focused, professional formatting.