Grounding means tying an AI model’s answers to specific, trusted material, such as documents you give it along with your question. The goal is an answer that reflects what those sources say. The model works from the pages in front of it instead of from whatever it picked up in training. For example, say you upload your organization’s volunteer handbook and ask how many hours new volunteers need. A grounded tool answers from the handbook and can point to the section it used.
Why it matters
Grounding is often done with retrieval-augmented generation. It is one of the main ways vendors try to make AI tools more accurate. It lowers the chance of a hallucination, but it does not remove it. A grounded tool can still misread a document or fill gaps with made-up details. When a vendor says its answers are grounded, ask which sources the tool draws on and whether each answer shows where it came from. Also ask what the tool does when your sources do not cover the question. Before you rely on a tool, check a few of its answers against the original documents yourself.
Published Updated