{"id":88156,"date":"2026-01-27T08:00:15","date_gmt":"2026-01-27T16:00:15","guid":{"rendered":"https:\/\/phisonblog.com\/?p=88156"},"modified":"2026-01-28T14:10:26","modified_gmt":"2026-01-28T22:10:26","slug":"accelerating-rag-workflows-with-next-gen-ssds","status":"publish","type":"post","link":"https:\/\/phisonblog.com\/de\/accelerating-rag-workflows-with-next-gen-ssds\/","title":{"rendered":"Beschleunigung von RAG-Workflows mit SSDs der n\u00e4chsten Generation"},"content":{"rendered":"

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Find out why high-performance storage is critical to getting the most effective real-time AI insights\u2014and how Phison makes it easy.<\/p>\n<\/blockquote>\n

AI is transforming how organizations interact with their data. But the next big leap isn\u2019t just in model development. It\u2019s in how those models access, retrieve and synthesize information on demand. That\u2019s where retrieval-augmented generation (RAG) comes in.<\/p>\n

RAG combines traditional generative AI with the ability to retrieve relevant context from external data sources in real time. This hybrid approach enables more accurate, up-to-date and context-aware responses, making it ideal for applications like enterprise search, conversational AI, customer support and scientific research. However, for RAG to deliver real value at scale, it needs more than just smart models. It needs exceptional storage performance to support fast, seamless access to large, unstructured datasets.<\/p>\n

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What makes RAG so demanding?<\/h3>\n

Unlike conventional generative models, which operate solely on pre-trained parameters, RAG injects external knowledge into the inference process. When a user query comes in, the system first retrieves relevant documents from a knowledge base, then feeds both the query and the retrieved data into a large language model (LLM)<\/a> to generate a response.<\/p>\n

This two-step process means the model must interact with massive, often heterogeneous datasets, ranging from internal wikis and support logs to academic journals and transaction records. These datasets need to be stored in a way that supports:<\/p>\n