Imagine a new employee who can instantly answer any question about your company's internal processes, not from general internet knowledge, but from your actual documentation, Slack messages, and internal wikis. That's what RAG (Retrieval-Augmented Generation) makes possible. Instead of relying solely on what a model learned during training, RAG fetches relevant information from your own data and includes it in the prompt. The model then answers grounded in your specific context. Companies use RAG to build internal Q&A bots, customer support agents, and research assistants that work with proprietary data the model was never trained on, without the cost and complexity of fine-tuning.