Artificial Intelligence changed dramatically after the rise of Large Language Models (LLMs) like GPT, Gemini, Claude, and Llama. Suddenly, AI could: write code summarize documents answer complex questions generate reports help with research even solve reasoning problems For a lot of people, it felt like AI became intelligent almost overnight. But once developers started building real products with these models, a major issue became impossible to ignore. LLMs are powerful, but they don’t actually know your data. They only know what they learned during training. And that limitation led to one of the most important ideas in modern AI engineering: Retrieval-Augmented Generation (RAG) Today, RAG is used almost everywhere in production AI systems, especially in: AI assistants enterprise search tools customer support bots research copilots coding assistants Before understanding how RAG works, it’s important to understand why it became necessary in the first place .…