In the rapidly evolving field of Generative AI (GenAI), one persistent challenge is the phenomenon of "hallucinations," where models generate plausible-sounding but incorrect or nonsensical information. This presentation delves into the innovative technique of Retrieval-Augmented Generation (RAG) as a solution to this problem. By integrating retrieval mechanisms with generative models, RAG significantly enhances the accuracy and reliability of AI outputs. Attendees will learn about the principles of RAG, its implementation strategies, and practical applications, gaining insights on how to effectively reduce hallucinations in their own GenAI applications.