If you’re worried about the security and accuracy of public AI models and want to create something that’s tailored to your data and use cases, then Retrieval Augmented Generation (RAG), might be the best way to get started with generative AI today without massive hardware investments or long procurement waits.

RAG lets you pair an off-the-shelf LLM with data in your own databases, and can be done on-premises, to create tailored, accurate responses that suit your needs. Even better, you can stand up a generative AI use case in days rather than months, and on your existing systems, eliminating the need for risky cloud deployments.

Join David O’Dell of Dell (no relation), as he explains to the Reg’s Tim Phillips how RAG can help you get started securely and quickly with gen AI, and where it can take you. We will cover:

• The blockers that stop you taking advantage of gen AI in your business
• How RAG can cure your AI implementation headache
• RAG AI development and deployment