Retrieval-Augmented Generation (RAG) is revolutionizing the capabilities of Generative AI by addressing critical limitations such as knowledge cut-offs, hallucinations, and lack of domain specificity. By integrating external knowledge sources with LLMs, RAG ensures outputs are more accurate, dynamic, and contextually relevant than ever before. In this session, we’ll begin with why RAG is essential for building scalable, trustworthy AI systems and before diving into advanced patterns like Modular RAG for adaptable designs, Graph RAG for structured data handling, and Voice RAG for audio-driven retrieval. Additionally, we’ll explore Corrective RAG, Branched RAG, and RAG-Fusion to tackle complex, multi-modal challenges. Whether you’re new to RAG or looking to refine your approach, this session will equip you with the tools and strategies to harness the full power of RAG workflows.