InfoWorld

The magic of RAG is in the retrieval

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 ...
InfoWorld

The limitations of model fine-tuning and RAG

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 ...
Computerworld

Despite its ubiquity, RAG-enhanced AI still poses accuracy and safety risks

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 ...
Forbes

The RAG Effect: How AI Is Becoming More Relevant And Accurate

The rapid advancements in artificial intelligence (AI) have led to the development of powerful large language models (LLMs) that can generate human-like text and code with remarkable accuracy. However ...
TechCrunch

Voyage AI is building RAG tools to make AI hallucinate less

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 ...
Bloomberg L.P.

Bloomberg AI Researchers Mitigate Risks of “Unsafe” RAG LLMs and GenAI in Finance

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 ...