Retrieval-Augmented Generation, or RAG, is a technique that connects a language model to your own knowledge. Before the model answers, the system retrieves the most relevant documents or records and passes them in as context, so responses are based on your reality rather than the model's general training.

It works by converting your content into embeddings stored in a vector database, finding the closest matches to a user's question, and giving those passages to the model along with the prompt. This keeps answers current, reduces hallucination, and lets the system cite its sources.

RAG is the backbone of most useful enterprise AI we build at arosplatforms. It avoids the cost and staleness of retraining, keeps sensitive data under your control, and gives business users answers they can trust and verify against the original documents.