If you’re already using AI and large language models (LLMs), you’re familiar with their impressive capabilities. But there’s another AI technology you might not know about: Retrieval Augmented Generation (RAG). In this blog series, we’ll explain what RAG is, how it works, and why it’s important for your organization’s AI strategy.

What Is RAG?

Retrieval Augmented Generation (RAG) is an advanced AI framework that enhances large language models (LLMs) by integrating an external information retrieval system. RAG broadens the scope of LLMs—which are confined to knowledge within their training data—by allowing them to access and retrieve up-to-date information from trusted external sources. This process, known as “grounding,” can improve the relevance and factual accuracy of responses to queries.

Why Should You Care?

RAG is emerging as an important technology because it enables organizations to enrich their LLMs with supplemental data to achieve specific goals while limiting risks. Because RAG data sets can be restricted to trusted sources such as verified documents, policies, frameworks, and databases, organizations can maintain a high degree of assurance that the inputs are accurate and relevant. 

RAG is especially useful when decisions require up-to-date information that can change often. It’s well suited for tasks that typically require input or validation from industry experts or third-party reference materials. By accessing external knowledge, RAG can deliver responses that are richer, more relevant, and based on current facts.

Stay tuned for our next post, where we’ll dive deeper into how RAG enhances LLMs for higher education.