The practice of giving an LLM access to verified source material at inference time, so its outputs cite specific evidence rather than relying on parametric knowledge that may be wrong, stale, or fabricated. In meeting copilots, grounding typically means: when the user asks "what did Alice commit to in the standup?" the system retrieves the relevant transcript chunks (via RAG), feeds them into the prompt, and instructs the model to answer only from the provided context — with citations back to the timestamp. Grounding is the primary defense against hallucination: a grounded model that doesn't find supporting evidence should say "I don't see Alice committing to anything in the available transcripts" rather than inventing a plausible-sounding commitment.