Best Practices for Using Cursor AI in Large-Scale Projects
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Summary
A comprehensive guide on effectively using Cursor AI in larger codebases, focusing on project organization, documentation management, and workflow optimization. The post details specific strategies for maintaining project structure, handling documentation, and ensuring consistent development practices with Cursor AI integration.
Prompt
Create a comprehensive project structure and architecture for a large-scale application with the following requirements: 1. Implement separate worker services for: - API calls and data collection - Analysis and OpenAI integration - Admin dashboard endpoints 2. Include configuration for: - Rate limiting - Error handling - Cost tracking - Scaling capabilities 3. Define the project structure with: - Milestone tracking - Documentation management - API endpoint documentation - Database schema organization Provide the necessary file structure, configuration templates, and architecture diagrams to implement this system.
Best Practices
Structured Project Documentation
Maintain a Project_milestones.md file referenced in .cursorrules
Regular Documentation Updates
Maintain and update Documentation.md with new functions and schemas
Worker Architecture Separation
Split workers for different responsibilities (API calls, analysis, admin dashboard)
Incremental Development
Work in small increments rather than large feature drops
Common Mistakes to Avoid
Avoid Large Feature Drops
Don't request or implement large feature sets at once
Skip Codebase Reindexing
Don't forget to regularly reindex the codebase in Cursor settings
Single Worker for Multiple Responsibilities
Avoid using a single worker for multiple types of operations
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