Retrieval-augmented generation (RAG) has become a go-to architecture for companies using generative AI (GenAI). Enterprises adopt RAG to enrich large language models (LLMs) with proprietary corporate ...

Here's the simple 30 second definition, A deeper dive will follow. RAG (Retrieval Augmented Generation) is the buzziest word on the GenAI internet right now, more jargon to confuse the uninitiated.

Few industries have the competitive pressure to innovate — while under as much public and regulatory scrutiny for data privacy and security — as the financial services sector. So, as companies ...

As financial services firms race to adopt and deploy AI solutions, the pressure is on to build systems that are not just powerful but also safe, transparent, and trustworthy. At Bloomberg, those have ...

To date, much of the early conversation about putting AI into production at scale has centered on the need for good prompt engineering — the ability to ask the right questions of this powerful ...

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 ...

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 ...

Beyond improvements in RAG, Fujitsu also offers a platform for various GenAI hybrid applications. This allows users to easily adopt suitable GenAI models, reducing the working hours required for GenAI ...