AI Builder Specialist Workshop
Master RAG Systems & AI Development
Transform into a skilled AI Builder with our comprehensive 10-week hands-on program. Perfect for developers, data analysts, and technical professionals seeking to master modern AI development.
Program Overview Video
Watch this comprehensive overview to understand the AI Builder Specialist program and the hands-on technical approach we'll use.
Program Overview
Transform into a skilled AI Builder with hands-on development skills
This comprehensive 10-week AI Builder Specialist program provides intensive hands-on training designed to transform participants into skilled AI Builders who can architect, develop, and deploy production-ready AI systems. Unlike traditional AI courses that focus on theory, this workshop emphasizes practical implementation using cutting-edge AI-powered development tools and real-world project development. Through a carefully structured progression from foundational RAG systems to enterprise-grade digital twin implementations, participants master the complete AI development lifecycle while building an impressive professional portfolio. What sets this program apart is its emphasis on AI-assisted development workflows throughout the learning journey. Students use GitHub Copilot for code generation, Claude Desktop for architectural planning, v0.dev for rapid web application development, and comprehensive MCP (Model Context Protocol) server integration that bridges traditional development with AI agent capabilities. Each week builds systematically on previous achievements, culminating in a production-ready digital twin system that serves as an AI-powered professional assistant, optimized for recruiter interactions and career advancement. The program's practical approach ensures graduates emerge with tangible, portfolio-worthy projects including enhanced RAG food systems with 35+ diverse cultural datasets, cloud-migrated applications demonstrating scalability best practices, professional web applications with real-time chat interfaces, and enterprise-grade digital twin systems with 24/7 accessibility and advanced monitoring. All projects emphasize real-world applicability, professional documentation standards, and industry-ready deployment practices that demonstrate comprehensive AI Builder competency to potential employers and clients.
Key Learning Areas
Hands-On AI Development: Master RAG systems, vector databases, and LLM integration from first principles to production deployment
MCP Server Development: Build Model Context Protocol servers for seamless AI integration with development tools
Digital Twin Implementation: Create sophisticated AI-powered professional avatars for interview preparation and career advancement
Production Deployment: Deploy and scale AI applications using modern cloud platforms and best practices
Advanced RAG Techniques: Implement LLM-enhanced RAG with query preprocessing and response optimization
Program Structure
Three comprehensive cycles designed for progressive mastery
Master AI development environment, build RAG systems from local to cloud, and deploy web applications using AI-powered development tools. This foundational cycle transforms participants from AI enthusiasts into practical AI Builders through hands-on implementation of core technologies. Starting with comprehensive development environment setup including VS Code Insiders, GitHub Copilot, and MCP server integration, participants quickly progress to building their first RAG system using ChromaDB and Ollama. The journey continues with cloud migration to Upstash Vector and Groq APIs, culminating in professional web deployment using AI-powered tools like v0.dev. Each week builds systematically on previous achievements, ensuring participants develop both technical competency and confidence in AI-assisted development workflows that will serve as the foundation for advanced challenges ahead.
Apply RAG skills to create sophisticated digital twin systems, integrate with development tools, and deploy production-ready AI applications. This advanced cycle elevates participants from foundational builders to enterprise-ready AI architects through sophisticated digital twin implementation. Participants design and build AI-powered professional assistants using STAR methodology for career data structuring, then integrate these systems across multiple platforms including VS Code GitHub Copilot and Claude Desktop. The cycle emphasizes real-world application through interview simulation using actual job postings, advanced query processing optimization, and production deployment with comprehensive monitoring. By combining technical excellence with practical professional application, participants emerge with not just advanced AI development skills, but also a powerful personal digital twin system that serves as both a career advancement tool and a testament to their AI Builder expertise.
Consolidate projects into professional portfolio and present to industry professionals with comprehensive demonstrations. This culminating cycle transforms participants from skilled practitioners into confident AI Builder professionals ready for career advancement and industry leadership. Participants integrate their complete project journey into a compelling professional portfolio using Next.js and modern web technologies, creating comprehensive case studies that demonstrate both technical depth and business impact. The experience concludes with live presentations to industry experts, providing valuable feedback and networking opportunities that often lead to career opportunities. Through personal branding development, technical communication refinement, and strategic career planning, participants emerge not just as AI Builders, but as recognized professionals positioned to lead the next generation of AI-powered development in their organizations and the broader tech industry.
Weekly Curriculum
Detailed breakdown of each week's learning objectives and deliverables
Overview
Master the Builder mindset and set up critical AI development environment with Visual Studio Code, GitHub Copilot, and MCP servers. Focus on AI-powered development workflows.
Learning Objectives
Reading Material
Classroom Activities
📋 Week Deliverable: AI Agents Research Report + Development Environment Setup
End of Week 1
Comprehensive research report with optional video presentation
Submit a comprehensive research report identifying and analyzing AI Agents (MCP Servers) for data analysis, complete development environment setup verification, and platform comparison analysis via Google Docs or Google NotebookLM video presentation
📝 Submission Requirements:
- 📋 OPTION A: Google Docs Research Report
- Create a shareable Google Doc with comprehensive sections:
- 📊 Section 1: AI Agents (MCP Servers) Analysis
- • Identify minimum 10 AI Agents (MCP Servers) suitable for data analysis
- • For each MCP Server create a detailed profile including:
- - Name and official documentation/repository link
- - Primary data analysis capabilities and specific use cases
- - Compatible platforms (VS Code, Claude Desktop, ChatGPT Developer Mode)
- - Installation complexity rating (1-5 scale with justification)
- - Data sources supported (databases, Excel, Google Sheets, APIs, etc.)
- - Real-world application scenarios for AI Builder workflows
- - Personal testing results and effectiveness rating (if tested)
- - Pros and cons based on research and/or hands-on experience
- 💰 Section 2: Platform Pricing & Subscription Comparison
- • Comprehensive comparison table covering:
- - Claude Desktop (Free tier capabilities and limitations)
- - ChatGPT Developer Mode (Pro/Plus subscription requirements and costs)
- - VS Code GitHub Copilot (Individual/Business/Enterprise pricing)
- - Other relevant AI platforms supporting MCP servers
- • Cost-benefit analysis for different AI Builder scenarios:
- - Students and individual learners
- - Professional developers and consultants
- - Small teams and startups
- - Enterprise implementations
- • ROI calculations and recommendations based on use case complexity
- � Section 3: Data Sources Compatibility Matrix
- • Create comprehensive table showing MCP server support for:
- - SQL databases (PostgreSQL, MySQL, SQLite, SQL Server, etc.)
- - NoSQL databases (MongoDB, Redis, Elasticsearch, etc.)
- - Cloud data services (AWS RDS, Google BigQuery, Azure SQL, etc.)
- - Spreadsheet platforms (Microsoft Excel, Google Sheets, Airtable)
- - API integrations and web scraping capabilities
- - File formats (CSV, JSON, XML, Parquet, Excel files, etc.)
- - Real-time data streams and webhooks
- • Scenario-based recommendations for different data analysis needs
- 🛠 Section 4: Development Environment Verification
- • Document your complete setup process with evidence:
- ✅ Node.js installation (version and screenshot/proof)
- ✅ Git configuration (version verification)
- ✅ VS Code Insider installation with GitHub Copilot enabled
- ✅ Claude Desktop installation and MCP server connections
- ✅ At least 3 MCP servers successfully connected and tested
- ✅ ChatGPT Developer Mode exploration (if accessible)
- • Include troubleshooting notes: challenges faced and solutions found
- • Configuration files and setup commands used
- 🎯 Section 5: Professional Analysis & Recommendations
- • Your top 5 recommended MCP servers for AI Builder workflows
- • Platform recommendation matrix based on different scenarios
- • Future trends and emerging tools in the MCP ecosystem
- • Personal learning insights and next steps in your AI Builder journey
- 📤 Google Docs Submission Requirements:
- • Document must be set to 'Anyone with the link can view'
- • Use professional formatting with clear headers, tables, and sections
- • Include working hyperlinks to all referenced tools and documentation
- • Minimum 2,000 words of substantive analysis and research
- • Submit the shareable Google Docs URL
- 🎥 OPTION B: Google NotebookLM Video Presentation + LinkedIn Post
- Create a comprehensive video presentation using Google NotebookLM:
- 📹 Video Content Requirements (8-12 minutes):
- • Introduction: Your AI Builder journey and Week 1 learning goals
- • MCP Servers Overview: Top 10 findings with 3-5 detailed showcases
- • Platform Comparison: Live demonstration of Claude Desktop vs ChatGPT vs VS Code
- • Data Analysis Focus: Specific examples of MCP servers for data workflows
- • Pricing Analysis: Cost-benefit breakdown for different user scenarios
- • Development Setup: Brief walkthrough of your environment configuration
- • Professional Insights: Key recommendations and future learning path
- • Call-to-Action: Engaging conclusion positioning yourself as AI Builder specialist
- 📱 LinkedIn Post Requirements:
- • Upload video directly to LinkedIn (not YouTube link)
- • Write engaging post copy (300-500 words) covering:
- - Your Week 1 learning experience and key discoveries
- - 3 most surprising findings about MCP servers for data analysis
- - Platform recommendations for different professional scenarios
- - Your commitment to the AI Builder learning journey
- - Call for engagement from your network
- • Use relevant hashtags: #AIBuilder #MCPServers #DataAnalysis #AITools #ProfessionalDevelopment #TechEducation
- • Tag relevant industry professionals, educators, or AI communities
- • Submit the LinkedIn post URL showing the published video and content
- ✅ Quality Standards for Both Options:
- • Demonstrate genuine research effort with multiple credible sources
- • Include practical, actionable insights for fellow AI Builder students
- • Show evidence of hands-on exploration and testing where possible
- • Use professional communication appropriate for technical audiences
- • Provide clear, well-organized information that others can follow and use
- • Include personal reflection on learning progress and next steps
Program Outcomes
What you'll achieve by completing this comprehensive program
Deploy 3+ Production AI Applications including local RAG system, web-enabled RAG deployment, and digital twin MCP server
Master AI Builder Toolkit with proficiency in Python, Next.js, TypeScript, vector databases, and LLM APIs
Create Professional AI Portfolio with comprehensive showcase of AI projects and proper documentation
Develop Interview-Ready Digital Twin as AI-powered professional assistant tested against real job postings
Establish AI Development Workflow with complete development environment and AI-assisted coding pipelines
Ready to Become an AI Builder?
Join our comprehensive 10-week program and master RAG systems, digital twins, and AI development. Build production-ready applications that showcase your skills.