How RAG Cuts Hallucinations in Generative AI Chatbots

Retrieval-augmented generation (RAG) reduces hallucinations in generative AI chatbots by grounding each response in retrieved source data instead of relying only on what the model learned during training. Before the model writes a reply, the system fetches relevant passages from a trusted knowledge store and passes them in as context. The model then answers from that evidence, which shrinks the room it has to invent facts.

This article looks at why hallucinations happen at the token level, how a RAG pipeline counters them, and the engineering choices that decide whether grounding actually holds up in production.