Supercharging LLM/GenAI with Retrieval-Augmented Generation (RAG) and Vector Databases by David Ding
Supercharging LLM/GenAI with Retrieval-Augmented Generation (RAG) and Vector Databases by David Ding
By David Ding
In this session, we shall delve into the intricate world of LLM (Large Language Models) and GenAI (Generative AI). These terms may seem esoteric, but our objective is to demystify them and provide a clear understanding of their implications.
We will explore the potent technique of Retrieval-Augmented Generation (RAG), a mechanism that can be likened to a supercharger for AI models. This technique enables the dynamic retrieval of pertinent information during the generation process, akin to having a vast library of knowledge at the disposal of your AI.
Furthermore, we will venture into the domain of vector databases. These are not ordinary databases; they are engineered to manage high-dimensional data, making them an ideal companion for RAG.
By the conclusion of this discourse, you will not only comprehend these advanced techniques but also envision their applications. Whether you are an AI enthusiast, a researcher, or a practitioner, we invite you to join us on this enlightening journey into the future of AI development. Let us augment our knowledge collectively.
About the Speaker
David Ding, Microsoft MVP, Author, Director, Lead Data Consultant
David is the director and lead data consultant. David is a certified Fabric Data Engineer, Data & AI developer with a Master’s Degree in Data Science. Previously, David held multiple senior business and technical positions. His book on Power Platform is part of his mission to help everyone get better with data.
