Jacob P. Portes

Oct 8, 2024 | databricks.com | Quinn Leng |Jacob P. Portes |Sam Havens |Matei Zaharia

Retrieval Augmented Generation (RAG) is the top use case for Databricks customers who want to customize AI workflows on their own data. The pace of large language model releases is incredibly fast, and many of our customers are looking for up-to-date guidance on how to build the best RAG pipelines. In a previous blog post, we ran over 2,000 long context RAG experiments on 13 popular open source and commercial LLMs to uncover their performance on various domain-specific datasets.

Aug 12, 2024 | databricks.com | Quinn Leng |Jacob P. Portes |Sam Havens |Matei Zaharia

Retrieval Augmented Generation (RAG) is  the most widely adopted generative AI use case among our customers. RAG enhances the accuracy of LLMs by retrieving information from external sources such as unstructured documents or structured data.