However, to make sure that answers to specific customer questions from customers, prospective customers and employees are correct and contextually appropriate, an enterprise needs to adopt a retrieval-augmented generation (RAG) AI workflow deployment architecture that leverages an enterprise own, internal datasets. This is critical for enterprises to make AI as accurate and relevant for their end-user customers as possible – and enterprise storage is at the center of it.

RAG AI is a storage-led advancement that augments AI models using relevant, proprietary data from an enterprise’s databases and files to improve the accuracy of AI and make AI working specifically for the enterprise. Utilizing authoritative, pre-determined, internal knowledge sources, it is designed to aggregate all the selected data to help keep the AI process fully up to date – all without needing to retrain the AI model, which is resource-intensive.

Life Without Enterprise Storage-enabled RAG AI Architecture

With their 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. Yet, even when the initial training phase goes extremely well, AI models continue to present challenges to enterprises. These data-driven, natural-language applications that are used to answer user questions need to be able to cross-reference authoritative information sources across your enterprise.

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 tend to make up the answer, in order to simply have an answer, even if that answer is based on false information. The implication is that a company’s customer could get completely wrong information, without knowing it.

However, with a RAG AI architecture that is an overlay on top of an enterprise storage infrastructure, enterprises can now cost-effectively add an information retrieval component to GenAI deployments and rely on their internal datasets.

RAG directly addresses this set of challenges. It helps eliminate AI hallucinations and potential inaccuracies. The AI learning model uses new knowledge from the RAG workflow, as well as its training data, to create much better, more trustworthy and spot-on responses.

Choose a Storage-led RAG AI Solution Without Any Specialized Equipment Needed

Existing enterprise storage systems can be used to implement RAG for this streamlining and honing of the process for making Agentic AI more accurate as well as relevant.

To take full advantage of RAG, you need the highest performance in your storage arrays as well as SLA-backed 100% availability. Indeed, 100% availability in enterprise storage is as mission critical as ever. It is also wise to add cyber storage resilience capabilities into your data infrastructure to ensure cyber recovery of data that is integral for GenAI applications.

Having an enterprise storage system that has a RAG workflow deployment architecture − and the right capabilities for AI deployments − will give you and your enterprise confidence that your IT infrastructure is able to harness large datasets and rapidly retrieve relevant information.

It’s been said that the way AI learns is semantic learning. It’s increasing knowledge based on prior knowledge. The vector databases that are used within RAG-optimized enterprise storage systems pull the data from all selected enterprise data sources and provide easy and efficient ways for the AI models to search them and learn from them.

You also want to look for an enterprise storage system that delivers the lowest possible latency. When you get your AI project off the ground and you are going to go into production mode, the speed and reliability of your storage infrastructure will matter.

Scalability of the enterprise storage infrastructure is also important. Let me explain: in general, an enterprise doesn’t have the capacity or capabilities to do the initial training of an LLM or SLM on its own the same way that hyperscalers do. Training an LLM requires robust and highly scalable computing.

Yet, the interconnection between a hyperscaler and enterprise – a seamless hand-off that is needed for GenAI to become more useful for enterprises in the real-world – calls for enterprises to have multi petabyte-scale, enterprise data storage. Large enterprises and mid-sized enterprises most often will need multi petabyte-scale storage solutions to adapt to the rapid changes with GenAI.

Key Takeaways

Thanks to RAG architecture, enterprise storage is now an integral element of GenAI implementation strategies. You need to fully utilize your own proprietary data sets that are stored in your company’s databases. The RAG workflows enable you to do it.

RAG adds a modern dimension to the business value of enterprise storage to augment GenAI success rates in enterprises. This involves leveraging enterprise storage for CIOs to utilize when building an ecosystem of an AI model optimized with RAG.

Ultimately, the accuracy of agentic AI depends on the enterprise’s storage-based RAG AI architecture.