A technique that gives AI models access to external knowledge by retrieving relevant documents before generating a response. Instead of relying only on what the model learned during training, RAG searches a knowledge base, finds relevant chunks, and includes them in the prompt as context.
Why it matters
RAG solves two major problems: hallucination (the model has real sources to reference) and knowledge cutoff (the knowledge base can be updated without retraining). It's how most enterprise AI actually works.