Why RAG is essential for workforce use of generative AI

Lurking amongst all the Generative AI (GenAI) buzzwords and acronyms is Retrieval-Augmented Generation (RAG). Software products have it. It’s important. We need it. Really.

But what is RAG, why is it important, why should we care, and what does it do for our businesses? 

Limitations without RAG

By itself, GenAI is not that smart about your organization's data. It must be trained to do its job and that training means data. Lots and lots of data. The better the data, the better the results. But without context, the data can generate results that are “off.” Like, you know it’s “kinda right,” but not really. 

We’ve all seen examples where tools like ChatGPTgive misleading answers that could easily be mistaken for accurate answers. Even one of these kinds of disconcerting errors is enough to put most people off. These "almost right" answers erode trust, much like receiving a subpar meal at a restaurant. Once trust is lost, it's tough to regain and you may never go back. Or maybe you go to another GenAI tool looking for better answers (more experience) but that’s a topic for another day…

Enter RAG

What we need is a way to improve the experience by training the GenAI with more data - but specifically, data trained to what the user is interested in. And to be clear, RAG doesn't actually train the model. It essentially injects additional contextual data into the conversation with a model to help it respond better. You might even say we want to augment how answers are Retrieved and Generated when working with a user. That is what RAG is and what RAG does. 

RAG stands for Retrieval-Augmented Generation and it works best in ongoing conversation. As interactions progress, RAG continues to adapt, learning from each exchange to provide increasingly accurate and meaningful responses. This isn’t just about better answers—it’s about a better, more human-like AI experience.

Why it matters

Suppose we have RAG enabled GenAI. Where does RAG come into play? RAG is all about human interaction with GenAI. It is the dynamic crafting of different answers and questions as human users interact with the GenAI technology. And to be clear, again, RAG itself isn't responding to the user, more so helping the LLM give a better response. As the conversation proceeds, RAG continues to get better and better.

RAG in practice

When RAG is integrated into your organization's GenAI tools, it ensures that your AI isn't just spitting out generic answers. Instead, it's providing responses tailored to the specific needs and context of your users. This means better customer service, more effective internal communications, and ultimately, a stronger trust in the technology.

At SurePath, we’ve integrated RAG into our solution to ensure that your workforce gets the most out of GenAI. Our approach allows your team to interact with AI in a way that feels natural and productive, ensuring that every interaction is as precise and relevant as possible. In our platform, this is done by setting up connections to data sources and then allowing, or blowing, access to specific RAG data (examples include: SharePoint or your website).

Contact our team to book a demo and learn how RAG can enhance your workforce’s GenAI experience.