This session will delve into the dynamic and rapidly advancing field of Retrieval Augmented Generation (RAG), an innovative approach that merges traditional information retrieval techniques with generative AI models to produce content that is more accurate, relevant, and grounded in factual data. It will begin with an overview of the fundamental paradigms of RAG, emphasizing its distinction from purely generative or purely retrieval-based systems. The session will then explore the key technologies that power RAG, including vector databases, dense retrievers such as ChromaDB and FAISS, and advanced transformer-based language models like BERT and GPT. Attendees will gain insights into RAG pipeline architectures, integration complexities, and deployment strategies in real-world environments. The session will conclude with a discussion of emerging trends and research directions, such as domain-adapted RAG, low-latency retrieval, and scalable knowledge grounding, offering a comprehensive understanding of both the conceptual foundations and practical applications of RAG in today's AI landscape.
Speaker(s): Dr. Rajanikanth Aluvalu
Virtual: https://events.vtools.ieee.org/m/486210