RAG (Retrieval-Augmented Generation)

A framework that combines AI generation with real-time information retrieval, allowing models to access current data and provide more accurate, grounded responses. Instead of relying solely on training data that might be outdated, RAG systems first search through up-to-date databases or documents to find relevant information, then use that context to generate responses. It's like giving an AI assistant access to a constantly updated library - when you ask a question, it first looks up the latest information before answering, rather than just relying on what it memorized during training. This approach significantly reduces hallucinations and enables AI systems to work with proprietary company data or recent information.