Quinn Leng

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.

Jan 22, 2024 | databricks.com | Ari Kaplan |Emily Hutson |Nicolas Pelaez |Quinn Leng

Generative AI has opened new worlds of possibilities for businesses and is being emphatically embraced across organizations. According to a recent MIT Tech Review report, all 600 CIOs surveyed stated they are increasing their investment in AI, and 71% are planning to build their own custom LLMs or other GenAI models. However, many organizations may lack the tools needed to effectively develop models trained on their own data.