Retrieval-Augmented Generation (RAG) architectures are enhancing AI's ability to address diverse challenges by integrating retrieval systems with generative models. This presentation focuses on the practical implementation of RAG, highlighting systems such as Naive RAG for straightforward single-hop queries and more sophisticated agentic and multimodal architectures for complex reasoning and cross-modal tasks. We will demonstrate the impact of these technologies with real-world applications.

Participants will develop a clean understanding of how to apply each RAG variant by learning their specific use cases and architectures. Key topics include foundational requirements like vector and graph databases, hybrid search methods, and multimodal embeddings. By blending theoretical insights with actionable strategies, this session equips the attendees with the tools to design smarter, context-aware, and scalable systems for tackling real-world issues effectively.