Private RAG - Unlocking Generative AI for the Enterprise

The release of ChatGPT in late 2022 opened the world’s eyes to the potential of generative AI. Large language models (LLMs) and chatbots can help your organization turbocharge productivity and unlock innovation. Retrieval augmented generation, also known as RAG, is a particularly interesting use case for enterprises like yours. RAG enables your enterprise to augment pretrained LLMs with your company’s private data to generate results that incorporate proprietary information from within your organization. For example, a chatbot that has access to your internal HR documents can help your employees with HR-related inquiries. And you can use an LLM that has access to your proprietary code to generate documentation. And so on.

At NetApp, we have adopted RAG for more efficient and relevant responses to documentation queries.

Unfortunately, enterprises have struggled to take advantage of these latest advancements due to very real concerns about data privacy and data governance. How does your enterprise prevent any leakage of sensitive information? How can your organization expose data to LLMs while maintaining data access controls and permissions across the data pipeline? At NetApp, we are in the business of helping you mitigate these concerns.

NetApp’s industry-leading data movement technologies can help you quickly and efficiently move data to where you need it, while preserving access controls and file system permissions with enterprise-caliber governance and logging. For example, you can use NetApp FlexCache^® technology to cache on-premises data in the cloud, where that data can be consumed by a cloud-based generative AI service.

If your internal data can’t be exposed to the cloud because of regulations or governance concerns, you can replicate the data or cache it to an on-premises high-performance AI compute platform instead. There it can be consumed by an LLM-based application that is completely under your organization’s control. We are exploring the use of these same data movement and caching technologies to efficiently keep up to date the vector stores that power LLMs.

You can also use NetApp data management technologies to protect the internal data that you’re using for RAG, no matter what the data format is. NetApp Snapshot™ technology enables the near-instant creation of space-efficient backup versions of vector stores and databases. So if your data ever becomes corrupted or compromised, it can be quickly and easily rolled back to a previous version. And with NetApp FlexClone^® technology, you can quickly and efficiently clone data volumes for development and testing (DevTest) workflows. NetApp BlueXP classification can even scan the data to confirm that it doesn’t contain any personally identifiable information (PII) or other sensitive information that shouldn’t be exposed to an LLM. If your dataset does contain sensitive data, you can use BlueXP classification to rapidly create a copy that doesn’t include the offending files.

To adopt RAG use cases, your organization needs an enterprise-caliber intelligent data infrastructure that spans clouds and data centers, preserves governance, and promotes sustainability—and that’s what NetApp offers. At NetApp INSIGHT^® 2023 in Las Vegas, we demonstrated NetApp powered RAG use cases in both Google Cloud and Amazon Web Services (AWS). And we’re currently validating an on-premises RAG architecture that uses popular open-source technology. We have also adopted RAG internally for documentation queries.

With NetApp’s intelligent data infrastructure, you can turn a world of disruption into opportunities. Explore more AI solutions from NetApp.