Databricks and Tonic.ai have partnered to simplify the process of connecting enterprise unstructured data to AI systems to reap the benefits of RAG. Learn how in this step-by-step technical how-to.

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 ...

Retrieval-augmented generation—or RAG—is an AI strategy that supplements text generation with information from private or proprietary data sources, according to Elastic, the search AI company. RAG ...

NetApp NTAP and NVIDIA NVDA have joined forces to enhance Retrieval-Augmented Generation (RAG) for generative AI applications. The collaboration integrates NetApp's intelligent data infrastructure ...

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, ...

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 ...

This free eBook that covers enhancing generative AI systems by integrating internal data with large language models using RAG is free to download until 12/3. Claim your complimentary copy of ...