The decades-long pursuit to capture, organize and apply the collective knowledge within an enterprise has failed time and again because available software tools were incapable of understanding the ...

The hype and awe around generative AI have waned to some extent. “Generalist” large language models (LLMs) like GPT-4, Gemini (formerly Bard), and Llama whip up smart-sounding sentences, but their ...

Retrieval Augmented Generation (RAG) is supposed to help improve the accuracy of enterprise AI by providing grounded content. While that is often the case, there is also an unintended side effect.

AI tends to make things up. That’s unappealing to just about anyone who uses it on a regular basis, but especially to businesses, for which fallacious results could hurt the bottom line. Half of ...

Enterprises are increasingly adopting hybrid AI models that combine generative AI with retrieval, reasoning, and memory modules for specific use cases. RAG fits perfectly into this architecture by ...

Though Retrieval-Augmented Generation has been hailed — and hyped — as the answer to generative AI's hallucinations and misfires, it has some flaws of its own. Retrieval-Augmented Generation (RAG) — a ...

NEW YORK – From discovering that retrieval augmented generation (RAG)-based large language models (LLMs) are less “safe” to introducing an AI content risk taxonomy meeting the unique needs of GenAI ...

Progress’ semantic and graph RAG approach—featuring MarkLogic Server 12—delivers 33% higher LLM accuracy and faster discovery for customers · GlobeNewswire Inc. Unlike other solutions, MarkLogic ...