Generative AI (GenAI) has garnered significant attention for its potential to revolutionize various industries, from creative arts to data analysis. However, organizations are realizing that implementing GenAI is not as easy as just asking ChatGPT a few questions. Providing the most relevant and accurate contextual data to the LLM is critical if organizations are going to realize the full benefits of GenAI. Retrieval Augmented Generation, or RAG, is a well understood and effective technique for augmenting the original user prompt with additional, contextual data. However, many examples of RAG grossly oversimplify the reality of enterprise data ecosystems. In this session, we will examine how a Logical Data Fabric can make RAG a practical reality in large, complex organizations and deliver AI-ready data that make RAG effective and accurate.