Over the past two weeks, we’ve explored what RAG is, its benefits, its applications for higher education, and how iseek.ai is innovating in this space. In this final post, we’ll bring it all together as we summarize the 7 ways iseek.ai’s RAG creates a superior LLM for professional and higher education.

1. Access to External Knowledge

iseek.ai’s RAG integrates large-scale retrieval systems into LLMs, enabling access to relevant information from external sources, such as a school’s proprietary curricular and assessment data, subscription databases, accreditation standards, competency-based education frameworks, and domain-specific ontologies. This augmentation ensures more accurate, context-specific responses tailored to a school’s needs.

2. Built-in Integrations

Through built-in integrations, iseek.ai provides off-the-shelf access to more than 30 platforms used widely in professional and higher education. The engine has extensive knowledge of the applications and the structure and type of data within them, as well as the necessary transformations to prepare the data for use as a knowledgebase for RAG LLMs.

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

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