RAG stands for Retrieval-Augmented Generation. RAG allows an LLM to search relevant information from an external knowledge source and use that information to generate an answer. Without RAG: With RAG: 1. Why do we need RAG? LLMs have several limitations. An LLM normally doesn’t know your company’s internal documents.Imagine you have an automotive company’s internal documents: Now a user asks: “What is the warranty period for a battery replacement?” A general LLM may not know your company’s specific warranty policy. Even if you provide the model with a huge prompt containing all documents, you have problems with: RAG solves this by retrieving only the relevant information. 2. RAG has two…