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.

To make sure the right information is retrieved, RAG uses advanced techniques such as word embeddings and vector search, which help the system identify the most relevant documents and passages for each query.

When a user submits a query, RAG augments the prompt with the most relevant external information, helping to ensure the accuracy of the response and grounding it in current, trusted knowledge.

What Other Controls Can Be Put in Place?

Institutions can apply “guardrails” to the system, such as instructing it to avoid generating speculative answers. This ability to exercise more control over the LLM’s output is one of the reasons RAG is particularly useful in regulated industries and high-stakes environments.

Ready to learn more? Tomorrow we’ll highlight the five key reasons higher education institutions should be thinking about RAG.