If you’re like me, the first few times you heard the term “RAG” (Retrieval Augmented Generation), you might have thought that someone was trying to clean up a mess. After reading through a bunch of technical explanations of RAG, I found that was pretty much the case. 

When you ask an AI Large Language Model (LLM) a question, the AI does some mathematical magic to figure out the most appropriate response from the universe of all possible responses. So far, so good. 

Unfortunately, that AI mathematical magic has some drawbacks. Because an AI is not really intelligent or conscious in any human sense, it doesn’t really know the meaning of anything that it says. Thus, while an AI might start in the normal universe, there’s no guarantee that it will stay there—and odds are very good that it won’t. 

This phenomenon is popularly called hallucination. A hallucination is what happens when an AI leaves the normal universe and wanders into chaos. The responses might be silly, nonsensical, or just plain wrong—or completely accurate in some other reality. Okay, I’ll admit that it’s hard to imagine any reality in which people should eat rocks or put glue in pizza sauce, but that’s the power of AI hallucinations: they boldly go where no one would want to go.

So where does RAG fit in? RAG creates boundaries so that the AI cannot wander off into chaos or start exploring some other reality at your expense. Rather than wandering the universe of all possible answers, RAG limits the AI to reality: no imaginary court cases in your list of precedents, no imaginary customers in the report to senior management, etc.  

SWIRL and RAG 

SWIRL makes extensive use of RAG to end your corporate data scavenger hunt. SWIRL installs easily onto any system and integrates with existing security protocols: your answers will always be drawn from the universe of data to which you have access, and then focused down to the specific world you care about. SWIRL does all this without copying data into vector databases or moving data to a vendor’s cloud. Rather, data stays where it is and SWIRL brings the AI to the data. You get accurate, relevant responses to your questions. 

SWIRL provides:

• Ability to connect to your choice of AI models
• AI-enhanced natural language search capabilities
• Retrieval Augmented Generation (RAG) without a vector database
• Context aware relevancy ranking
• Personalized results
• Scalability
• Freedom from data duplication
• Data security

So That’s RAG 

And that’s RAG in a nutshell: a technique SWIRL uses for making sure an AI doesn’t feed you convincing sounding nonsense. RAG really does clean up the mess an AI makes.  

To find out more about how SWIRL can end your data scavenger hunt, contact us.