AI Enterprise Accelerator Training

Course Outline

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.