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The Evolution of RAG

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.