Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a "retrieval-augmented generation" chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation. Source - @akshay_pachaar