With demand for enterprise retrieval augmented generation (RAG) on the rise, the opportunity is ripe for model providers to offer their take on embedding models. French AI company Mistral threw its ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

A behind-the-scenes blog about research methods at Pew Research Center. For our latest findings, visit pewresearch.org. In April 2023, Pew Research Center published an analysis of mission statements ...

RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...

Retrieval Augmented Generation: What It Is and Why It Matters for Enterprise AI Your email has been sent DataStax's CTO discusses how Retrieval Augmented Generation (RAG) enhances AI reliability, ...

Google researchers introduced a method to improve AI search and assistants by enhancing Retrieval-Augmented Generation (RAG) models’ ability to recognize when retrieved information lacks sufficient ...

Search is dead, long live search! Search isn’t what it used to be. Search engines no longer simply match keywords or phrases in user queries with webpages. We are moving well beyond the world of ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now When large language models (LLMs) emerged, ...

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 ...

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 ...