Why do you use Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) combines a language model with an external knowledge base. The model first retrieves relevant documents or passages from a large corpus and then conditions its generation on both the input prompt and the retrieved content. This approach improves factual accuracy, reduces hallucinations, and allows the model to stay up‑to‑date without retraining. For example, in a customer support chatbot, RAG can fetch the latest product documentation and generate precise answers, whereas a vanilla LLM might produce outdated or incorrect information. RAG is especially useful when the task requires up‑to‑date knowledge or domain‑specific facts that are not fully captured in the model’s training data.