Embedding Selection for RAG Systems

Retrieval-Augmented Generation (RAG) has emerged as a critical approach for extending large language models beyond their training data. At the heart of every effective RAG implementation lies a important decision: which embedding model to use. This choice will impact your system's performance, costs, and scalability.