Advantages of RAG include its ability to handle vast knowledge bases, support dynamic updates, and provide citations for ...

That's what we're really doing here: improving the mechanics of AI trust. If the LLM is reliably pulling from RAG data, you can then optimize that data through various "advanced RAG" techniques such ...

For years, search engines and databases relied on essential keyword matching, often leading to fragmented and context-lacking results. The introduction of generative AI and the emergence of ...

Any data used for RAG must be converted to a vector database, where it's stored as a series of numbers an LLM can understand. This is well-understood by AI engineers ... a small chunk of text.