Day 0 – Optional Virtual Prep - Friday, Nov 21st (2 PM - 5 PM ET)
AI Concepts & Readiness (Half-Day)
Purpose: Ensure all participants start with a common vocabulary, conceptual grounding, and core AI knowledge.
AI Foundations (90 min)
- What AI really means for business
- Key concepts: AI, ML, Generative AI, inference, LLMs, prompting
- Business-relevant use cases
- AI myths and realities
AI Landscape & Environments (90 min)
- The spectrum: models, assistants, agents
- How we interact with AI: chatbots, IDEs, Python, APIs
- Staying current with rapid AI change
- What to expect in the 3-day bootcamp
Day 1 – Models & Retrieval-Augmented Generation (RAG) - In person (Dec 1st)
Purpose: Build fluency with AI models and how to extend them with your own business data.
Understanding AI Models (90 min)
- How LLMs work: embeddings, transformers, attention
- Model landscape: reasoning, frontier, open-source models
- Training, tuning, and parameters
Using AI Models (90 min)
- Finding, running, and qualifying models
- Hands-on: fine-tuning models for business contexts
Understanding RAG (90 min)
- Why models alone are not enough
- RAG explained and alternatives
- Vector databases and data pipelines
Applying RAG (90 min)
- Ingesting and parsing enterprise data
- Prompting templates and frameworks
- Hands-on: building agentic RAG with curated datasets
Day 2 – AI Agents & Model Context Protocol (MCP) - In-Person (Dec 2nd)
Purpose: Learn to build AI agents that can reason, use tools, and connect to business systems.
AI Agents Part 1 (90 min)
- Motivations and use cases for agents
- What agents are and how they work
- Chain of thought, memory, data management
- Hands-on: creating an AI agent that leverages tools
AI Agents Part 2 (90 min)
- Multi-agent design patterns
- Using RAG with agents
- Hands-on: Creating an AI agent to process structured business data with canonical queries
- Future of AI agents in enterprise workflows
MCP Part 1 (90 min)
- What MCP is and why it matters
- MCP architecture, transports, features
- Frameworks for MCP implementation
- MCP clients and servers
- Hands-on: Building an MCP server and client and using them with an agent
MCP Part 2 (90 min)
- Connecting to MCP servers
- Common patterns and pitfalls
- Security and predictions for MCP adoption
- Hands-on: How to leverage and use public MCP servers in your AI processes
Day 3 – AI for Productivity & Capstone Project In-Person (Dec 3rd)
Purpose: Apply AI directly to software development, collaboration, and real-world business scenarios. Build a complete working AI application as a capstone.
Accelerating the SDLC with GitHub Copilot – Part 1 (90 min)
- Copilot for planning, exploration, directives
- Hands-on: AI in planning, development, and bug-fixing
Accelerating the SDLC with GitHub Copilot – Part 2 (90 min)
- Advanced Copilot features (Agent Mode, Vision, Edit)
- Hands-on: testing, refactoring, documentation, onboarding
Capstone Project Part 1 (90 min)
- Designing an AI-powered application
- Choosing a model, prompts, MCP integration
- Hands-on: build the base app, MCP server, and agent
Capstone Project Part 2 & Wrap-Up (90 min)
- Hands-on: Adding RAG for local data
- Hands-on: Building a chat interface
- Hands-on: Hosting on a platform for shared use
- Wrap-up: presenting solutions, next steps for AI adoption
Why This Program Is Valuable to Businesses
- Immediate Productivity Impact: Employees return with skills to use AI in daily workflows, accelerating development, analysis, and communication.
- Future-Proofing Talent: Teams gain exposure to leading AI frameworks and practices, positioning your organization to adapt quickly as AI evolves.
- Capstone Deliverable: Each participant builds and deploys a functioning AI application they can demonstrate and extend at work.
- Hands-On & Practical: Significant course time in each session is dedicated to labs and project work.
