
Retrieval-Augmented Generation (RAG) has emerged as a powerful technique to ground large language models (LLMs) with specific data sources. By leveraging external information, RAG addresses the limitations of foundational language models that are trained offline on broad domain corpora and suffer from outdated training sets. This article explores the workings of RAG, its approach to overcoming training challenges, and the