Large Language Models (LLMs) have revolutionized the field of natural language processing, but their true potential lies in their ability to be integrated into real-world applications. In this talk, we'll explore the power of Retrieval-Augmented Generation (RAG) models, which combine the strengths of LLMs with the flexibility of retrieval-based approaches. We'll delve into the design and implementation of RAG models, discussing the benefits and challenges of using pre-trained LLMs as a foundation. Through a hands-on example, we'll demonstrate how to build a RAG model that can be applied to a variety of real-world problems, from text generation to question-answering. By the end of this talk, attendees will gain a deeper understanding of how to unlock the full potential of LLMs and create practical, effective RAG models that drive business value.