How does grounding a prompt with provided context reduce hallucination?

Grounding supplies the facts the model should use directly in the prompt, e.g. retrieved documents in a RAG system, and instructs it to answer only from that context. This shifts the task from recalling parametric memory (error-prone, stale) to reading and synthesizing supplied text, which cuts fabrication. Effective grounding adds explicit rules: "Answer only using the context below; if the answer is not present, say you don't know," and asks for citations or quotes tied to source passages so claims are verifiable. It does not eliminate hallucination: the model can still misread, over-generalize, or blend outside knowledge, and bad or irrelevant retrieval poisons the answer. Pair grounding with an explicit refusal path and, ideally, automated checks that every claim maps to a source.