Course Overview

Welcome to the VIBE CODING EXPERT COURSE! This comprehensive guide outlines a repeatable, structured process for working with AI coding assistants to build production-quality software. While we'll use the example of building a Supabase MCP server with Python, the same process applies to any AI coding workflow.

By the end of this course, you'll have mastered a structured approach to working with AI coding assistants, allowing you to build better software, faster, and with fewer headaches.

Skool Community Integration

This course has been specially adapted for the Skool community platform to maximize engagement and learning. You'll benefit from:

All course resources, recordings, and materials will be organized within your Skool community for easy access and reference.

Complete Course Outline

Module 1: Golden Rules of AI Coding

Learning Objectives:

  • Understand the high-level principles that guide effective AI coding workflows
  • Learn to implement best practices for project organization and documentation
  • Develop strategies for breaking down complex problems into AI-manageable pieces
  • Master efficient conversation patterns with AI coding assistants

Lesson Plan:

Lesson 1.1: Introduction to AI Coding Golden Rules

Overview of the eight golden rules and why they matter for AI-assisted development

Lesson 1.2: Project Organization with Markdown

How to use README.md, PLANNING.md, and TASK.md effectively

Lesson 1.3: Code Modularity and File Management

Strategies for keeping files under 500 lines and organizing modular code

Lesson 1.4: Conversation Management with AI Assistants

Best practices for starting fresh conversations and avoiding context degradation

Lab Activities:

Lab 1: Setting Up Your First AI-Assisted Project

In this hands-on session, you'll:

  • Create a project structure following golden rules
  • Set up README.md, PLANNING.md, and TASK.md with templates
  • Practice conversation patterns with AI assistants on simple tasks
  • Evaluate and refine your approach based on the quality of AI responses

Assessment:

Assignment 1: Golden Rules Implementation

Create a small project that demonstrates the implementation of all eight golden rules. Document your process and share with the community for feedback.

Module 2: Planning & Task Management

Learning Objectives:

  • Master the art of project planning with AI coding assistants
  • Learn to create and maintain effective PLANNING.md and TASK.md documents
  • Develop strategies for task breakdown and prioritization
  • Understand how to guide AI assistants using planning documentation

Lesson Plan:

Lesson 2.1: Effective Project Planning with AI

Techniques for collaborating with AI assistants on project planning and scoping

Lesson 2.2: Creating Comprehensive PLANNING.md Files

Structure and content of effective planning documents that guide AI assistants

Lesson 2.3: Task Management Strategies

Best practices for TASK.md and keeping AI assistants focused on current priorities

Lesson 2.4: Dynamic Task Updates and Discoveries

How to handle new requirements and discoveries during development

Lab Activities:

Lab 2: Planning a Supabase MCP Server Project

In this hands-on session, you'll:

  • Collaborate with an AI assistant to plan a Supabase MCP server project
  • Create a comprehensive PLANNING.md with architecture, tech stack, and constraints
  • Develop an initial TASK.md with your project roadmap
  • Practice prompting AI to update and manage tasks as the project evolves

Assessment:

Assignment 2: Project Planning Documentation

Create complete planning documentation for your Supabase MCP server project, including PLANNING.md and TASK.md. Then, simulate a project change and demonstrate how you would update these documents.

Module 3: Global Rules for AI IDEs

Learning Objectives:

  • Understand the concept and importance of global rules in AI IDEs
  • Learn to configure global rules across different AI coding platforms
  • Master techniques for enforcing consistent code quality and practices
  • Develop custom rule sets for different project types and requirements

Lesson Plan:

Lesson 3.1: Introduction to Global Rules

Understanding how global rules enhance AI coding assistant capabilities

Lesson 3.2: Configuring Rules in Popular AI IDEs

Step-by-step configuration in Cursor, Windsurf, Cline, and Roo Code

Lesson 3.3: Rule Categories and Best Practices

Structuring rules for project awareness, code structure, testing, and documentation

Lesson 3.4: Customizing Rules for Project Types

Developing specialized rule sets for different languages and frameworks

Lab Activities:

Lab 3: Implementing Global Rules

In this hands-on session, you'll:

  • Configure global rules in your preferred AI IDE
  • Test rule effectiveness with sample coding tasks
  • Refine rules based on AI assistant performance
  • Create custom rule sets for Python-based MCP server development

Assessment:

Assignment 3: Global Rules Configuration

Develop a comprehensive set of global rules for your AI IDE that enforces all the golden rules. Test your rules with a series of coding tasks and document how they improved the AI's output quality.

Module 4: Configuring MCP

Learning Objectives:

  • Understand the Model Context Protocol (MCP) and its capabilities
  • Learn to configure MCP in different AI coding environments
  • Master various MCP operations including file system, web search, and Git
  • Develop strategies for extending AI capabilities through MCP servers

Lesson Plan:

Lesson 4.1: MCP Fundamentals

Introduction to Model Context Protocol and how it enhances AI capabilities

Lesson 4.2: MCP Configuration Across AI IDEs

Setting up MCP on Cursor, Windsurf, Cline, and Roo Code

Lesson 4.3: MCP Operations Deep Dive

Using MCP for file operations, web searches, Git commands, and more

Lesson 4.4: Custom MCP Servers

Introduction to creating and customizing MCP servers for specialized tasks

Lab Activities:

Lab 4: Hands-on with MCP

In this hands-on session, you'll:

  • Configure MCP in your preferred AI IDE
  • Practice file system operations through MCP
  • Use web search capabilities to find documentation
  • Perform Git operations through MCP prompts

Assessment:

Assignment 4: MCP Integration Challenge

Create a series of prompts that demonstrate effective use of MCP capabilities. Document how each MCP feature enhances your development workflow with practical examples.

Module 5: Initial Prompt Crafting

Learning Objectives:

  • Master the art of crafting effective initial prompts for project setup
  • Learn techniques for providing examples and documentation to AI assistants
  • Understand how to set clear project requirements and expectations
  • Develop strategies for guiding AI response quality from the start

Lesson Plan:

Lesson 5.1: The Anatomy of an Effective Initial Prompt

Key components and structure of high-quality project kickoff prompts

Lesson 5.2: Documentation Reference Techniques

Methods for incorporating documentation and examples in your prompts

Lesson 5.3: Setting Project Requirements

How to clearly communicate project goals, constraints, and expectations

Lesson 5.4: Initial Response Evaluation

Techniques for assessing and redirecting AI output from initial prompts

Lab Activities:

Lab 5: Crafting Initial Prompts

In this hands-on session, you'll:

  • Analyze and evaluate example initial prompts
  • Craft your own initial prompt for the Supabase MCP server project
  • Test prompt effectiveness with different AI assistants
  • Refine your prompt based on the quality of responses

Assessment:

Assignment 5: Initial Prompt Challenge

Create a comprehensive initial prompt for your MCP server project. Submit both your prompt and the AI's response, along with your analysis of the response quality and suggested improvements.

Module 6: Modular Prompting Process

Learning Objectives:

  • Master the modular prompting approach for ongoing development
  • Learn techniques for focused, single-task prompts
  • Understand when and how to start fresh conversations
  • Develop strategies for maintaining documentation during incremental development

Lesson Plan:

Lesson 6.1: Single-Task Focus Strategy

How to craft focused prompts that yield consistent results

Lesson 6.2: File-Specific Modifications

Techniques for directing AI to update specific files effectively

Lesson 6.3: Conversation Management

When and how to start fresh conversations for optimal results

Lesson 6.4: Documentation Updates

Strategies for maintaining README.md, PLANNING.md, and TASK.md during development

Lab Activities:

Lab 6: Modular Prompting Practice

In this hands-on session, you'll:

  • Practice crafting focused, single-task prompts
  • Implement feature updates using modular prompting
  • Experience when and how to refresh conversations
  • Update documentation after feature implementations

Assessment:

Assignment 6: Modular Development Challenge

Implement three sequential features in your MCP server using the modular prompting approach. Document your prompts, AI responses, and how you managed conversation context throughout the process.

Module 7: Testing Strategies

Learning Objectives:

  • Master the art of testing AI-generated code
  • Learn effective test prompting techniques
  • Understand best practices for unit testing in AI coding workflows
  • Develop strategies for mock testing external services

Lesson Plan:

Lesson 7.1: Testing Fundamentals in AI Coding

Best practices for incorporating testing in AI coding workflows

Lesson 7.2: Crafting Test Prompts

How to prompt AI assistants to write comprehensive unit tests

Lesson 7.3: Mock Testing External Services

Techniques for isolating tests from external dependencies

Lesson 7.4: Test-Driven Development with AI

Implementing TDD principles with AI coding assistants

Lab Activities:

Lab 7: Implementing Tests for MCP Server

In this hands-on session, you'll:

  • Create test templates for your MCP server
  • Prompt AI to generate unit tests for existing features
  • Implement mock testing for Supabase interactions
  • Practice test-driven development for a new feature

Assessment:

Assignment 7: Testing Suite Implementation

Develop a comprehensive testing suite for your MCP server. Include at least three types of tests: happy path, edge case, and error handling. Document your prompt strategies for generating these tests.

Module 8: Docker Deployment

Learning Objectives:

  • Master Docker containerization with AI assistance
  • Learn to prompt AI for creating Dockerfiles and container configurations
  • Understand deployment options for containerized applications
  • Develop strategies for managing environment variables in deployed applications

Lesson Plan:

Lesson 8.1: Docker Fundamentals

Introduction to Docker and containerization concepts

Lesson 8.2: Dockerfile Creation with AI

How to prompt AI assistants to create effective Dockerfiles

Lesson 8.3: Container Build and Run

Commands and best practices for building and running Docker containers

Lesson 8.4: Cloud Deployment Options

Overview of platforms for deploying containerized applications

Lab Activities:

Lab 8: Docker Deployment of MCP Server

In this hands-on session, you'll:

  • Create a Dockerfile for your MCP server
  • Build and test your Docker container locally
  • Configure environment variables for deployment
  • Explore cloud deployment options

Assessment:

Assignment 8: Full Deployment Project

Containerize your MCP server with Docker and document the entire process. Include Dockerfile, build commands, run instructions, and a deployment guide for at least one cloud platform.

Weekly Schedule

Week Module Live Session Labs Assignments
Week 1 Module 1: Golden Rules of AI Coding Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 2 Module 2: Planning & Task Management Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 3 Module 3: Global Rules for AI IDEs Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 4 Module 4: Configuring MCP Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 5 Module 5: Initial Prompt Crafting Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 6 Module 6: Modular Prompting Process Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 7 Module 7: Testing Strategies Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST
Week 8 Module 8: Docker Deployment Monday, 7:00 PM EST Wednesday, 7:00 PM EST Due Sunday, 11:59 PM EST

Additional Community Activities

  • Office Hours: Fridays, 5:00 PM - 6:00 PM EST
  • Peer Code Review Sessions: Saturdays, 2:00 PM - 4:00 PM EST
  • AI Coding Challenge: First Sunday of each month
  • Guest Expert Webinars: Announced two weeks in advance

Capstone Project

Throughout the course, you'll build a complete Supabase MCP server with Python. This project will be developed incrementally as you progress through the modules, applying each new concept you learn.

Project Deliverables:

  • Fully functional Supabase MCP server
  • Comprehensive documentation
  • Complete test suite
  • Docker containerization
  • Deployment guide
  • Presentation of your work to the community

Final Showcase

At the end of the course, you'll have the opportunity to showcase your completed project to the entire Vibe Coding Experts community. This will include a demonstration of your MCP server's capabilities and a presentation of your development process.

Community Engagement

Weekly Check-ins

Every Monday, share your progress, challenges, and wins from the previous week in our dedicated thread. This creates accountability and provides opportunities for community support.

Peer Feedback Sessions

Participate in structured peer code reviews to get feedback on your work and help others improve their projects. These sessions develop critical code review skills.

Community Challenges

Participate in monthly coding challenges that test your AI prompting skills in timed, focused exercises. Winners will be featured in our community spotlight.

Resource Sharing

Contribute to our community knowledge base by sharing useful articles, tools, and resources related to AI coding assistants and development best practices.

Study Groups

Form or join study groups of 3-5 members to tackle the course material together. Benefits include:

  • Collaborative problem-solving
  • Shared learning resources
  • Increased accountability
  • Expanded network of AI coding enthusiasts

Resources

AI Coding Tools:

Cursor

https://cursor.sh

AI-native code editor built for pair programming

Windsurf

https://www.codeium.com/windsurf

Development environment with built-in AI assistance

Cline

https://cline.bot

Terminal-first AI coding assistant

Roo Code

https://roocode.com

AI-powered code editor and assistant

MCP Resources:

Model Context Protocol

https://github.com/modelcontextprotocol/python-sdk

Official Python SDK for the Model Context Protocol

MCP Server Examples

https://github.com/modelcontextprotocol/mcp

Example implementations of MCP servers for various services

Supabase Resources:

Supabase Documentation

https://supabase.com/docs

Official documentation for Supabase

Supabase Python Client

https://github.com/supabase-community/supabase-py

Official Python client for Supabase

Instructor and Support

Lead Instructor

Our lead instructor is an experienced AI coding expert with extensive background in production software development and AI assistant optimization.

Office Hours: Fridays, 5:00 PM - 6:00 PM EST

Teaching Assistants

Our team of TAs will provide additional support during labs, review assignments, and assist with technical questions.

TA Support Hours: Mon-Fri, 12:00 PM - 8:00 PM EST

Community Support

In addition to instructor and TA support, you'll have access to our active community of AI coding enthusiasts. The community is an invaluable resource for troubleshooting, idea sharing, and collaborative learning.

Course Conclusion and Next Steps

Congratulations on completing the VIBE CODING EXPERT COURSE! You've learned a structured approach to working with AI coding assistants that will help you build production-quality software more efficiently.

By following the principles and practices outlined in this course, you'll be able to:

Advanced Paths

After completing this course, consider these advanced learning paths:

  • Custom MCP Server Development
  • Advanced AI Prompt Engineering
  • AI-Assisted Code Architecture
  • AI Testing Automation

Remember that mastering this workflow takes practice. Start with smaller projects and gradually apply these techniques to more complex codebases as you get comfortable with the process.

© 2023 VIBE CODING EXPERT COURSE

Adapted for Skool Community Platform