Large Language Models (LLMs) have significantly advanced natural language understanding and generation, powering a wide range of applications from chatbots to content creation. As their adoption grows, so does the need for more accurate, current, and explainable outputs, especially in high-stakes or domain-specific contexts. Retrieval-Augmented Generation (RAG) is an emerging approach that enhances LLMs by combining them with real-time, external knowledge sources, resulting in more grounded and reliable responses. This session will explore the foundations of RAG, its architectural components, and advanced techniques. We will also discuss why RAG is increasingly critical for production use. Attendees will walk away with a strong conceptual understanding of how to apply RAG to build more effective and scalable LLM applications.