In the first four posts of this series, we’ve covered a lot about Retrieval Augmented Generation (RAG)—how it works, why it matters, and the benefits it brings. Now, we’re shifting gears to show you how we’re taking RAG to the next level at iseek.ai. We’ve built a two-step retrieval process designed to deliver even more precise and relevant results.

Before the retrieval process even begins, iseek.ai converts source content—such as curricular materials or assessment data—into vector embeddings. We enhance these embeddings with domain-specific concepts, enabling the system to quickly group similar content or materials matching a particular topic-based query.

Step 1: The Search

The first step of the process is the search itself. iseek.ai starts by identifying the most relevant results based on the query. It creates a universe of content that’s deeply focused on a specific discipline, contextually appropriate, and closely aligned with the query and domain-specific needs.

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.