Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is a technique that gives a language model access to your own documents or data at query time, so answers are grounded in your information rather than only what the model learned in training. It is the most common way to build an accurate, up-to-date AI feature on top of a general model.
RAG works in two steps: first retrieve the passages from your own knowledge base that are relevant to a question, then hand those passages to the language model as context so its answer is based on them.
It is popular because it solves two problems at once: it reduces hallucination by anchoring answers in real source material, and it lets a model use private or recent data it was never trained on, without the cost and risk of fine-tuning. For most products that need an AI feature grounded in their own content, RAG is the first thing to reach for.
