RAG is an approach that combines large language models (LLMs) with retrieval-augmented generation to improve accuracy and reduce AI hallucinations when working with large amounts of data, especially proprietary or sensitive information. This approach allows companies to integrate real-time, proprietary data into AI responses, enhancing accuracy and personalization. RAG can be used in various sectors such as ecommerce, healthcare, customer service, and more, offering benefits like enhanced shopping experiences, improved patient outcomes, and increased customer satisfaction. Companies that have high hopes for how AI will improve their customer interactions and operational efficiency should consider using RAG to unlock the ability to build world-class personalized customer experiences at scale.