GenAI Fundamentals : The Databricks Approach

Learning Objectives

Distinguish between traditional ML pattern matching and GenAI contextual understanding through real customer service scenarios

Understand the three-phase GenAI evolution (Simple LLM → RAG → Agent Systems) and recognize the capabilities and limitations of each approach

Explain how LLMs, embeddings, and RAG work together using analogies to create human-like problem-solving capabilities

Evaluate why Databricks provides advantages for enterprise GenAI implementation, including unified data access, governance, and cost control

Demonstrate how to use AI/BI Genie to convert natural language queries into SQL and leverage AI-assisted notebooks for code generation and debugging in Databricks

Explore the Databricks Mosaic AI Agent Framework by building a basic AI agent that can reason through problems and execute Python code using available tools

Imagine your customer service team handling 50,000 daily inquiries with 90% accuracy instead of 70%. What if your AI could understand "My order is late for my daughter's wedding" and automatically fix the shipping, update billing, and provide emotional reassurance - all in one response?

This isn't fantasy - it's the difference between traditional ML pattern matching and true GenAI intelligence. Through Sarah's journey at RetailCorp, you'll discover why your current keyword-based systems feel "dumb" to customers, and how to architect GenAI solutions that think like your best customer service reps.

You'll learn about the evolution from Simple LLMs to RAG applications to full Agent systems, understanding when and why to use each approach.

Most importantly, you'll see how Databricks Mosaic AI turns this vision into enterprise reality without data governance nightmares or cost explosions.