Retrieval Augmented Generation (RAG) systems face challenges in managing and retrieving the right data from extensive knowledge bases, leading to the need for AI data segmentation. Proper segmentation involves organizing data into logical partitions, which is crucial for ensuring security by preventing data leakage in multi-tenant environments and improving retrieval accuracy in domain-specific searches. By segmenting data, RAG systems can enhance retrieval quality through better contextualized TF-IDF calculations and allow for precise control using metadata filters to refine searches based on specific document attributes. This approach is essential in multi-tenant applications and domain-specific knowledge bases, as it enhances the security, accuracy, and performance of RAG systems. Tools like Ragie facilitate this process by supporting AI data segmentation through partitions and metadata filtering, demonstrating significant benefits for data isolation, relevancy, and control in data retrieval tasks.