The data that is stored in vector databases is key to the success of generative AI (GenAI) for enterprises in all industries. Up-to-date, private data in company data sources, including unstructured data and structured data, is what is required during AI inferencing to make GenAI models more accurate and relevant.
To make the data systematically useful for GenAI after the initial training of an AI model, a new framework is needed. Thanks to advancements in enterprise data storage, the retrieval-augmented generation (RAG) architecture is the proven framework that has emerged to meet this critical need.
RAG 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. In short, a well-done RAG storage deployment will aggregate all the selected data to help keep the AI process fully up to date.
A prime illustration is RAG equipping enterprises to auto-generate more precise, reliable answers to queries from customers or employees. What is essentially happening is that RAG enables AI learning models, including large language models (LLMs), like ChatGPT, to reference information that goes beyond the data on which it was trained. This data is the proprietary data that enterprises have in their data sources, which sit on storage infrastructure.
Think of it this way: RAG is helping to customize general AI models with a company’s most updated data, and the implication is that the LLM will always leverage those data sources to keep up to date. It is gaining much-needed contextual awareness. This also applies to small language models (SLMs) too.
Left on their own, LLMs and SLMs are either static or only leverage publicly available information, such as information on the Internet. 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. This dynamic has put enterprise storage at the center of the adoption of GenAI in enterprise environments through the RAG architecture.
Requirements for Storage Infrastructure with GenAI
Storage infrastructure needs to be cyber secure and 100% available. No down time! No compromises of the data! It needs to be flexible, cost-effective, and be able to operate in a hybrid multi-cloud environment, which is increasingly the standard environment for large enterprises today.
You also want to look for a storage system that delivers the lowest possible latency. Believe me, you want your storage infrastructure to be high performance and ultra-reliable when you get your AI project off the ground and are going to go into production mode. By the way, having an RAG configuration that gets all the data sources you need across multiple vendors and your data in your hybrid multi-cloud environment is critical to accurate AI.
Having an enterprise storage system that has a RAG workflow deployment architecture − and the right capabilities for AI deployments − will give you and your organization confidence that your IT infrastructure is able to harness large datasets and rapidly retrieve relevant information. The vector databases that are used within RAG-optimized enterprise storage systems pull the data from all selected data sources and provide easy and efficient ways for the AI models to search them and learn from them.
It’s been said that the way AI learns is semantic learning. It’s, basically, increasing knowledge based on prior knowledge. The AI model has its “brain” that was trained on gigantic amounts of publicly available information – AI training, usually done in a hyperscaler environment – but when it comes into the enterprise, your need to get that data from your enterprise data sources, so AI can be updated and customized – AI inferencing. Thus, the AI model can make sense of not only words, but also the proper context. During the AI inferencing phase, the AI model applies its learned knowledge. You don’t want your AI to be hallucinating, do you?
Scalability of the enterprise storage infrastructure cannot be ignored in this situation either. Sure, the typical enterprise won’t have the capacity or capabilities to do the initial training of an LLM or SLM on its own the way hyperscalers do. Training an LLM requires robust and highly scalable computing.
Nonetheless, 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 petabyte-scale, enterprise-grade data storage. Even medium-sized enterprises need to consider petabyte-scale storage to adapt to the rapid changes with AI.
The value of data goes up when you turn your storage infrastructure from a static backstop into a next-generation, dynamic, super-intelligent platform to accelerate and improve AI digital transformation.