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.

As the publishing industry navigates the rapidly changing landscape of the information age, it faces both exciting opportunities and new challenges. Emerging technologies, particularly AI-driven solutions, offer a powerful way for publishers to adapt and thrive. By unlocking the potential of their vast content libraries, AI is transforming how publishers approach content discoverability and creation. Read on to explore how these innovative tools are helping publishers stay ahead of the curve and harness the full value of their content.

Improving Content Discoverability

Artificial intelligence is revolutionizing how publishers analyze their content. With advanced search capabilities, AI can sift through entire content repositories, uncovering key concepts and topics within. This allows publishers to quickly identify what has already been published and where there are gaps in their offerings.

Helping your students perform better benefits your school in a number of ways. Student engagement on campus rises. You foster an ideal culture of learning. You attract enthusiastic new students year after year. But improving student performance doesn't always mean assigning extra homework or testing more rigorously. Read on to learn how you can elevate performance with the help of AI solutions.

Put Student Learning Intervention Strategies in Place

A student learning intervention can take many forms. You may help students to pick a new major, sign up for remedial courses, or work with a tutor. But time is of the essence. AI solutions can help you identify learning gaps quickly and precisely, so you can individualize remediation plans for each student's needs.