Using the .cursorrules file when working with the Cursor IDE

I have been using the Cursor IDE for a while now. I only recently discovered the .cursorrules file feature, as it's not well highlighted in the documentation. This file, placed in the workspace root, helps provide additional context to the LLM, allowing to specify coding standards, common packages, and documentation.

This feature might address a key issue with Cursor: it only follows existing coding patterns within the file being edited.

One limitation is that only one .cursorrules file can exist per workspace, which complicates things for a large monorepo with multiple languages compared to smaller, consistent repositories.

Additionally, the documentation mentions this file is for chat modalities only, not tab completion. However, this post states that pinning it in an open tab allows it to influence tab completion too.

Here's why you might want to use it:

1. Customized AI Behavior: .cursorrules files help tailor the AI's responses to your project's specific needs, ensuring more relevant and accurate code suggestions.
2. Consistency: By defining coding standards and best practices in your .cursorrules file, you can ensure that the AI generates code that aligns with your project's style guidelines.
3. Context Awareness: You can provide the AI with important context about your project, such as commonly used methods, architectural decisions, or specific libraries, leading to more informed code generation.
4. Improved Productivity: With well-defined rules, the AI can generate code that requires less manual editing, speeding up your development process.
5. Team Alignment: For team projects, a shared .cursorrules file ensures that all team members receive consistent AI assistance, promoting cohesion in coding practices.
6. Project-Specific Knowledge: You can include information about your project's structure, dependencies, or unique requirements, helping the AI to provide more accurate and relevant suggestions.

By creating a .cursorrules file in your project's root directory, you can leverage these benefits and enhance your coding experience with Cursor AI.

This repository on GitHub hosts a great library of .cursorrules files, to learn from and adapt.

There is also a growing listing of .cursorrules at cursor.directory where users can contribute.

Also very useful is the Cursor Rule Maker that helps you tailor a .cursorrules file to your needs.

You can prompt it in natural language, for example "I am building a GenAI RAG application with Python that uses Azure OpenAI. I want to use Azure Document Intelligence to convert PDF documents to JSON." and it will create something like the following:

# .cursorrules file for GenAI RAG Application with Azure OpenAI and Document Intelligence

- Specify clear problem definitions and conduct thorough dataset analysis before starting the project.
- Use modular code structures: separate files for models, data loading, and data conversion processes.
- For Azure OpenAI API integrations:
  - Use proper authentication mechanisms and handle sensitive data securely.
- When converting PDF documents to JSON using Azure Document Intelligence:
  - Leverage Azure SDKs or REST APIs for document processing.
  - Implement error handling for document parsing and conversion tasks.
- Use configuration files (e.g., YAML) for managing API keys and endpoint settings.
- Employ version control (e.g., Git) for tracking changes and collaborative development.
- Follow PEP 8 style guidelines for Python code.
- Ensure proper logging and monitoring to debug and track processing pipelines.

# Integration and performance guidelines
- Optimize data loading and processing with efficient data structures.
- Use asynchronous programming or multi-threading for I/O-bound operations to enhance performance.
- Implement caching and lazy loading for frequently accessed documents.

- Document the system architecture and code for maintainability and easier onboarding of new developers.

# Testing and validation
- Develop unit and integration tests to ensure robust API interactions and document conversion processes.
- Validate input data and handle exceptions gracefully to provide user-friendly error messages.