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The Evolution of RAG
Fri, 25 April
In this session, you’ll explore how Retrieval-Augmented Generation (RAG), the primary architectural pattern of the Generative AI revolution, has evolved over the past few years. RAG has become the go-to approach for implementing systems that control, direct, and manage Large Language Models (LLMs).
We’ll dive into the advancements and variants of RAG that have emerged, showcasing reference architectures and real-world implementations. You’ll gain insights into how this technology has shaped the AI landscape and see its practical applications across various industries, helping businesses unlock new capabilities by combining retrieval systems with the power of generative AI.
Target Audience: This session is primarily aimed at DeepTech Professionals and AI Researchers, as it covers advanced developments in RAG architecture and its impact on AI systems. Software Architects are the secondary audience, as they will benefit from understanding how to design and implement RAG systems for practical AI applications. DataTech Professionals working on AI and data-driven systems may also find value in learning how RAG enhances retrieval and generation processes in modern AI solutions.
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About the speaker
Brian Sam-Bodden
Senior Applied AI Engineer
Brian Sam-Bodden is a Senior Applied AI Engineer at Redis as well as an author, instructor, speaker, developer advocate and open source contributor and Java Champion who has spent over twenty years crafting software systems. He holds dual bachelor’s degrees from Ohio Wesleyan University in computer science and physics. Brian is a frequent speaker at user groups and conferences nationally and abroad and is the author of “Beginning POJOs: Spring, Hibernate, JBoss and Tapestry”, co-author of the “Enterprise Java Development on a Budget: Leveraging Java Open Source Technologies” and a contributor to O'Reilly's “97 Things Every Project Manager Should Know”.
