RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...

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 ...

Large language models are fast becoming a key building block in new information systems for administrative staff and clinicians at hospitals and health systems. This form of artificial intelligence ...

A new buzzword is making waves in the tech world, and it goes by several names: large language model optimization (LLMO), generative engine optimization (GEO) or generative AI optimization (GAIO). At ...

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.

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 Large language models (LLMs) have seen ...

Large language models can generate useful insights, but without a true reasoning layer, like a knowledge graph and graph-based retrieval, they’re flying blind. The major builders of large language ...

As more organizations implement large language models (LLMs) into their products and services, the first step is to understand that LLMs need a robust and scalable data infrastructure capable of ...