Enterprise storage has moved into a significant position in the world of generative AI (GenAI). The reason is that GenAI will not be as useful, relevant and trustworthy for enterprises without the use of a storage-centric Retrieval-Augmented Generation (RAG) architecture for workflow deployment. RAG is the key for enterprises to fully leverage GenAI. Consequently, RAG is on the way of becoming part of almost every enterprise AI project.
RAG is designed to improve the accuracy and relevancy of AI models with up-to-date, private data from multiple company data sources, including unstructured data and structured data. RAG augments AI models using relevant and private data retrieved from an enterprise’s vector databases. Vector databases are offered by various vendors, such as Oracle, PostgreSQL, MongoDB and DataStax Enterprise. These are used during the AI inference process that follows AI training.
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As part of a GenAI framework, RAG enables enterprises to auto-generate more accurate, more informed, and more reliable responses to user queries. As market analysts have attested, RAG offers a high-value proposition, and it benefits from an enterprise storage solution, such as InfiniBox, that delivers high levels of performance, 100% guaranteed availability, scalability, and cyber storage resilience
It enables AI learning models, such as a Large Language Model (LLM) or a Small Language Model (SLM), to reference information and knowledge that is beyond the data on which it was trained by giving it a comprehensive set of all the appropriate enterprise data sources. It not only customizes general models with a company’s most updated information, but it also reduces the need for continually re-training AI models.
AI models power intelligent chatbots and other natural language processing applications, which are used to answer user questions by cross-referencing authoritative information sources. Without iterative updating and fine-tuning of these models, which are static or only tend to leverage publicly available data, the return of a query will often deliver incorrect or misleading results, which are known as “AI hallucinations.”
AI hallucinations appear as factually inaccurate content, false attributions, or citation of nonexistent information. RAG workflow has emerged as a key tool to bridge this gap and provide continued refinement of data queries and leverage all relevant data sources in the enterprise, so AI gets all the information it needs to make the right decisions.
RAG reduces the prevalence of “AI hallucinations,” a common problem with Gen AI. It combines the power of generative AI models with enterprises’ active private data to produce continuously updated, correctly informed responses to live queries, such as ChatGPT, which is an LLM. By continuously refining a RAG pipeline with new data, enterprises can ensure the accuracy of AI.
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The role of enterprise storage in the AI era will only escalate in importance, especially over the next five years when the rate of data-centric innovation is expected to speed up even more. In the hybrid multi-cloud environments in which most enterprises operate, enterprise storage will become unstoppable as a force that is central to the world of GenAI.
CIOs will have to reassess how they approach enterprise-class data storage and what enterprise storage solutions they turn to in order to evolve with the increasing need for petabyte-scale storage infrastructure. Future-proofed solutions that maximize flexibility will get a second look.
Enterprises deploying RAG don’t need to run out and spend big budgets for new storage infrastructure. Enterprises deploying RAG will be able to do so on their existing storage infrastructure.
With Infinidat’s RAG architecture, enterprises utilize Infinidat’s existing InfiniBox and InfiniBox SSA enterprise storage systems, any enterprise data sitting in a hybrid multi-cloud configuration with InfuzeOS Cloud Edition, and any NFS data that is sitting on non-Infindat storage products as the basis to optimize the output of AI models, without the need to purchase any specialized equipment.
Overall, the opportunity for Infinidat to expand its impact in the enterprise market with its highly targeted RAG reference architecture is significant.
To put it all in perspective, just as GenAI has become an unstoppable force for transformation, enterprise storage has emerged as unstoppable as a lever to help unleash the full potential of GenAI technology in enterprise environments.
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