This presentation explores Retrieval-Augmented Generation (RAG) and its critical role in building advanced AI agents. We begin with an overview of what text embeddings are and how they are used, followed by a discussion on storing and retrieving information from vector databases. You’ll learn how integrating a RAG-based search tool enables AI agents to access external, up-to-date knowledge—overcoming the limitations of their training data to provide more accurate, specific, and reliable responses. We'll also walk through a simple code example to demonstrate these core concepts in action.