How can we ensure AI systems are accurate, transparent, and up-to-date? All Large Language Models (LLMs) have a knowledge cut-off and lack insight into your company's internal workings. Even the leading models have hallucination rates that can't be ignored, yet they offer enormous potential for productivity, efficiency, and creativity.

This is where Retrieval-Augmented Generation (RAG) comes in: enhancing LLMs through targeted information retrieval. We'll explore the architecture of RAG-based systems. We'll learn how it fills knowledge gaps and improves the accuracy and reliability of generative AI systems.