Scaling Enterprise RAG with Accelerated Ethernet Networking and Networked Storage

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In the era of generative AI, where machines are not just learning from data but generating human-like text, images, video, and more, retrieval-augmented generation (RAG) stands out as a groundbreaking approach.  A RAG workflow builds on large language models (LLMs), which can understand queries and generate responses. However, LLMs have limitations, including training complexity and…

Hello, I like your post.
it is mentioned "While traditional enterprise apps can compress data and store it for efficient retrieval, to support indexing and semantic search, RAG-based databases can expand to more than 10x larger than the original text documents and their associated metadata. This leads to significant data growth and storage challenges. "
=> Could you connect me with a specialist who could explain me that?
Best Regards