By Eric Herzog, CMO at Infinidat
Generative AI (GenAI) has found an unexpected “partner” in a type of information technology that CIOs tend not to prioritize for AI – enterprise storage. Because data is central to the activation and guidance of GenAI, the storage infrastructure that stores all of an enterprise’s data has taken on a new role as the foundation for retrieval-augmented generation (RAG).
RAG is highly relevant for any enterprise that is planning to leverage GenAI for customized responses to queries. RAG is a GenAI-centric framework for augmenting, refining and optimizing the output of AI models, such as Large Language Models (LLMs) and Small Language Models (SLMs).
This is what you need to know: RAG is a storage infrastructure-led architecture to improve the accuracy of AI. It enables enterprises to ensure that the answers from AI models remain relevant, up-to-date, and within the right context. With their powerful, generative AI capabilities, AI models power intelligent chatbots and other natural language processing applications, which are used to answer user questions by cross-referencing authoritative information sources.
Many AI models are initially trained on extremely large datasets that are usually publicly available. However, to make answers to customer questions highly specific and contextually correct for your enterprise, RAG redirects an AI model (i.e. LLM) to retrieve private and proprietary data out of an organization’s databases. This is the key to making the AI more accurate, as it utilizes authoritative, pre-determined, internal knowledges sources – all without needing to retrain the AI model, which is resource-intensive.
CIOs and business leaders who oversee GenAI projects can breathe a sigh of relief. Thanks to this new option of extending the usefulness of the enterprise storage infrastructure to make AI more accurate, enterprises can now cost-effectively add an information retrieval component to GenAI deployments and rely on their internal datasets so as to not expose their enterprise to public inaccuracies. As part of a transformative effort to bring one’s company into the AI-enhanced future, it’s an opportunity to leverage intelligent automation with RAG to create better, more accurate and timely responses.
No Specialized Equipment Needed
Part of the good news of a RAG workflow deployment architecture is the fact that it does not require any specialized equipment. Existing enterprise storage systems, such as the InfiniBox® and the InfiniBox™ SSA, can be used to implement RAG for this value-added component of streamlining and honing the process for making GenAI more accurate and relevant.
RAG brings a whole new dimension to the business value of enterprise storage to increase the success rates of GenAI within enterprise-sized organizations. This involves leveraging enterprise storage for CIOs to use when creating an AI model ecosystem that is optimized with RAG. It is becoming a “must-have.”
To make the most of RAG, you want to have the highest performance in your storage arrays as well as SLA-backed 100% availability. Never before has 100% availability in enterprise storage been as mission-critical as it is today in a GenAI-infused world. It is also smart to look to add cyber storage resilience capabilities into your data infrastructure to ensure cyber recovery of data that is integral for GenAI applications.
No matter whether the data is all in a data center or in a hybrid multi-cloud configuration, a RAG workflow deployment architecture will work. A cloud edition of an enterprise-grade storage solution integrates seamlessly with the cloud, simplifying and accelerating the rollout of RAG for enterprises. This complements the work that hyperscalers are doing to build out AI models on a larger scale to do the initial training of the AI models.
Why is RAG So Important to GenAI?
Even when the initial training phase goes extremely well, AI models continue to present challenges to enterprises. They too commonly can present “AI hallucinations,” which are basically inaccurate or misleading results from a GenAI model. When it does not have the information it needs, an AI model will make up the answer, in order to simply have an answer, even if that answer is based on false information. This has eroded the trust that people have in early deployments of GenAI.
AI models have a tendency to provide inaccurate answers because of confusion about terminology. They can also deliver out-of-date information or a response from a non-authoritative source. The implication is that a company’s customer could get completely wrong information, without knowing it. What a ‘data disaster’ that is!
RAG directly addresses this set of challenges. It’s a reliable method to eliminate the “AI hallucinations” and ensure more informed responses to queries via a GenAI application for enterprises. The AI learning model uses the new knowledge from the RAG workflow, as well as its training data, to create much better responses. This will improve the level of trust that people will have in GenAI.
Key Takeaways
With the RAG architecture, enterprise storage is now an essential element in the GenAI deployment strategy. Use it to continuously refine a RAG pipeline with new, up-to-date data to hone the accuracy of AI models.
Remember, don’t under-utilize your enterprise’s own proprietary datasets stored in your databases. You need to connect the dots between GenAI and your data infrastructure. The enterprise storage-led RAG approach helps you.
To optimize your storage systems for this enhancement, look for industry-leading performance, 100% availability and cyber storage resilience. They make you RAG-ready.
Metaphorically, RAG is like the “new oil” to make the GenAI engine run better with trusted data on top of an always-on data infrastructure.
About Eric Herzog
Eric Herzog is the Chief Marketing Officer at Infinidat. Prior to joining Infinidat, Herzog was CMO and VP of Global Storage Channels at IBM Storage Solutions. His executive leadership experience also includes: CMO and Senior VP of Alliances for all-flash storage provider Violin Memory, and Senior Vice President of Product Management and Product Marketing for EMC’s Enterprise & Mid-range Systems Division.
