Tengyu Ma

This talk explores Retrieval Augmented Generation (RAG) as a key technology for enabling large language models (LLMs) to access and utilize proprietary enterprise data. The speaker argues that RAG offers a more efficient, cost-effective, and manageable approach compared to fine-tuning or relying solely on long context windows. The presentation also touches upon the evolution of RAG, advancements in embedding models, and future directions for improving retrieval accuracy and simplifying workflows.