Retrieval-Augmented Generation (RAG) Introduction

Overview

Retrieval-Augmented Generation (RAG) Introduction is a four-hour class that provides a practical overview of RAG and its role in building smarter, data-aware AI applications. Participants will learn how large language models (LLMs) integrate with retrieval systems to deliver accurate, grounded responses, exploring key concepts such as chunking, data preparation, vector databases, and embeddings. The session also introduces advanced enhancement techniques like re-ranking and context optimization, giving attendees a solid foundation for designing and implementing effective RAG-based solutions.

Topics Discussed:

1. LLM Overview and application design
2. Retrieval-Augmented Generation (RAG) concepts
3. Chunking and data preparation
4. Vector Databases and Embeddings
5. Re-ranking and other enhancement techniques

This course is included in the AI Annual Learning Pass.

Duration: 

1/2 Day