Large language models are capable drafting and retrieval tools. They become difficult to trust when they are asked to answer anything, from anywhere, with no review path.
Grounding is the practice of connecting a model to approved sources and making those sources part of the response path. It does not eliminate error, but it gives teams something they can inspect and improve.
Equally important is the job definition. An assistant that summarizes a policy, drafts a reply, or retrieves a procedure is easier to evaluate than one that is expected to replace an entire function.
Evaluation should be treated as product work. A small, representative set of questions, a human review loop, and a place to capture failures will do more for quality than another round of prompt polishing alone.