Generation 2: RAG — The Era of Grounded Knowledge (2022–2023) In the first generation of AI, models were like brilliant students locked in a room with no internet. They had incredible reasoning skills, but their knowledge was frozen in time (their "training data cutoff"). If you asked about a company memo written yesterday or a news event from this morning, they would either apologize or, worse, confidently hallucinate an answer. Enter RAG: Retrieval-Augmented Generation. RAG is the architectural pattern that connects a Large Language Model (LLM) to external, real-time data. Instead of relying solely on its internal memory, the model "looks up" relevant information before it speaks. What RAG does? RAG connects the system to live documents, APIs, web data and database So instead of: Answer = Model Memory It becomes: Answer = Retrieved Data + Model Reasoning RAG grounds responses in the retrieved context.…