We’ve had a few questions about how RAG compares to LLM fine-tuning. Here’s the breakdown:

How Are They Different?

Both RAG and LLM fine-tuning allow institutions to enhance their LLMs, but in different ways. Fine-tuning modifies an LLM’s internal parameters using additional training data. RAG, on the other hand, supplements the model’s internal memory with non-parametric data retrieved from external sources.

Which One Is Better?

Fine-tuning can work well for specific tasks that don’t need constant updates, but RAG is better when information changes frequently. For dynamic environments, RAG’s ability to retrieve up-to-date information in real time is a big advantage.

By now, you've likely grasped the basics of RAG and its potential in higher education. But why should your institution invest in RAG-enhanced technology? In this post, we’ll break down five key ways RAG can help to ensure your school’s LLM aligns with evolving academic standards. 

1. Real-time, Current Information: RAG technologies can give you assurance that your LLM has access to the latest, most accurate information.

2. Verified Responses: With the ability to trace the source of information, RAG lets you cross-reference LLM outputs to ensure accuracy and relevance.

   Read more: Top 5 Reasons Higher Ed Needs RAG-Enhanced LLMs

   ---

In our last post, we introduced RAG and talked about why it matters. As a quick recap: RAG boosts traditional LLMs—which rely on pre-existing data in their training—by pulling in info from outside sources. This expanded view makes them especially useful for applications where up-to-date, domain-specific knowledge is key.

Today, we’ll dive deeper into how RAG works, with a focus on higher education. 

How Does RAG Work?

RAG works by pulling in information from trusted external sources to enhance the knowledge of LLMs. For professional and higher education applications, RAG might tap into proprietary databases, library subscription resources, accreditation standards, competency frameworks, and subject-specific ontologies.

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.