GenAI’s Impact on Learning Experiences

Since the introduction of OpenAI’s ChatGPT in 2022, the power of Large Language Models (LLMs) has permeated many of our everyday lives in ways we wouldn’t have imagined 10 years ago. For one, we have now grown accustomed to receiving plain-language answers to our questions instead of digging into a list of references to extract answers. This shift is driven by the availability of Generative AI (GenAI), which enables systems to provide direct, concise, and tailored answers to user queries. As technology and society move forward, our content ecosystems must adapt to this new reality.

Look at every major search engine and notice that the first result typically is an AI-generated summary of results, or a direct answer to your query. This is true even for privacy-focused search engines like DuckDuckGo. This means that we are growing increasingly accustomed to receiving direct and relevant responses to our searches rather than needing to sift through a list of potential resources. In the learning ecosystem, we are responsible for providing accessibility, quality, and convenience for our learners.

So, how do we make GenAI answers a reality when managing a wealth of secured, proprietary content, while major LLMs are trained on publicly available content? There are two primary ways to allow GenAI to leverage our proprietary content:

• Training a model directly, which can often be out of reach or simply impractical

• Leveraging Retrieval Augmented Generation (RAG)

Let’s take a look at what a RAG system entails:

What Is RAG?

RAG combines the strengths of GenAI with content curation to allow for LLMs to create a seamless user experience that provides accurate and relevant answers based on your organization’s proprietary content. Here’s a simple diagram of what a RAG workflow looks like:

User Query -> Semantic Breakdown of Query -> Semantic Content Search/Retrieval -> LLM Response Generation With Additional Context of Retrieved Content

The Importance of High-Quality, Proprietary Content

To make the most of GenAI, the technology will need access to well-maintained, well-defined, and preferably well-structured content, and having a central content management system as the source of truth for your content facilitates these things with very little overhead. Xyleme’s Intelligent Learning Content Management System (iLCMS) allows you to single source your proprietary content in a well-defined, well-structured manner while providing standardized, structured content analytics. A platform that offers these things as a foundation is crucial for building effective RAG systems that can provide well-informed, accurate GenAI responses sourced from your proprietary content and intellectual property.

Provide the Rocket Fuel for Your AI Systems

As we’ve said, quality content helps GenAI create quality answers, but to get the most out of that quality content, it should be well-structured, well-defined, well-described, and accessible. Platforms like Xyleme give you the foundation to build upon for your propriety content and use it as the rocket fuel for your AI-powered tools and solutions. Content in the Xyleme platform is well-structured XML with a well-defined schema and is well-described with deep metadata layers that give your content a wealth of semantic context beyond just what the words say. Using this platform allows you to unlock the full potential of using GenAI to create a seamless learning experience for your users, even when those answers must be informed by secured, proprietary content.

How Xyleme Facilitates AI-Powered Systems

In addition to providing the foundational content requirements for high-quality GenAI systems, Xyleme also facilitates the technical requirements needed to build and fuel powerful AI solutions in your content ecosystem. Xyleme Syndicate’s cutting-edge AI Search feature provides Syndicate users with semantic search capabilities but has also been specially designed with RAG enablement in mind. Xyleme Syndicate AI Search allows for RAG systems to do semantic, vector queries against your inventory of proprietary content (or a subset of it; you’re in control) to create context-enhanced user prompts with your RAG retriever, an essential piece of functionality to any RAG-capable GenAI tool.

To summarize, many signs point to the future of learning being more tailored, substantive, and, crucially, more rapid. The wealth of knowledge in your proprietary content need not be locked into single-modality experiences, because you can free it with the power of GenAI, RAG, and the Xyleme Platform. Together, these will unlock a new era of accessibility for learners.