A practical overview of security architectures, threat models, and controls for protecting proprietary enterprise data in retrieval-augmented generation (RAG) systems.
What if the way we retrieve information from massive datasets could mirror the precision and adaptability of human reading—without relying on pre-built indexes or embeddings? OpenAI’s latest ...
Forbes contributors publish independent expert analyses and insights. I am an MIT Senior Fellow & Lecturer, 5x-founder & VC investing in AI RAG add information that the large language model should ...
In the digital age, the ability to find relevant information quickly and accurately has become increasingly critical. From simple web searches to complex enterprise-knowledge management systems, ...
Widespread amazement at Large Language Models' capacity to produce human-like language, create code, and solve complicated ...
Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...
Generative AI entered the global consciousness with a bang at the close of 2022 (cue: ChatGPT), but making it work in the enterprise has amounted to little more than a series of stumbles. Shadow AI ...
What happens when your AI-powered retrieval system gives you incomplete or irrelevant answers? Imagine searching a compliance document for a specific regulation, only to receive fragmented or ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback