Code 100x Faster with AI, Here’s How (No Hype, FULL Process)
AI Summary
Video Summary: Effective AI Coding Workflow
- Introduction to AI Coding Assistants
- Importance of having a clear process when using AI tools like Windsurf or Cursor
- Common issues faced without a defined workflow
- Three Promises of the Workflow
- Simplified setup
- Practical application: building a Superbase MCP server
- Versatile process applicable to any development and AI IDE
- Golden Rules for Effective AI Coding
- Use markdown documents for instructions and context
- Avoid overwhelming the AI with long prompts and complex instructions
- Keep code files under 500 lines
- Ask for one feature or task at a time
- Request tests for every new feature implemented
- Provide specific, detailed requests
- Document process with comments and higher-level documentation
- Manage security—never trust AI with environment variables
- Project Planning
- Create planning and task markdown files before coding
- Use AI to assist in generating these files
- Keep high-level context to guide AI in project execution
- Global Rules Setup
- Defining system prompts for AI
- Instructions on maintaining project consistency
- MCP Servers Configuration
- Use of MCP servers to enhance AI IDE capabilities
- Setup for file management, web search, and Git integration
- Initial Prompting and Iteration
- Providing detailed prompts and examples for project build
- Guiding through documentation, tasks, and project specifics
- Testing and Deployment
- Importance of creating comprehensive tests
- Using Docker for deployment
- Writing documentation throughout the process
- Conclusion
- Reflection on the effectiveness of the approach
- Call for viewer engagement and tips on developing AI coding workflows.