AI Vocabulary · 03 Working with AI
What is Grounding?
Tying a model's answer to specific, trusted sources, so it works from facts you provided instead of memory.
Grounding means giving a model concrete sources, such as your documents, a database or search results, and instructing it to base its answer on them, ideally with citations. The opposite is answering from training memory alone, where gaps and outdated knowledge lead to hallucinations.
RAG is the most common technical way to ground answers in your own material. Web search does the same for current information past the knowledge cutoff. A grounded answer is only as good as its sources, so the quality of what you put into the context matters.
Why it matters for you
For anything factual, such as prices, policies, contracts and statistics, ask for answers grounded in sources and with references you can click. Ungrounded answers belong in brainstorming, not in decisions.
Own it
Grounding is how your own knowledge shapes AI output. Well-kept documents and a clear single source of truth make every AI tool you use more accurate.
Where it comes from
The concept comes from cognitive science. In 1990, Stevan Harnad described the "symbol grounding problem": how symbols can get their meaning from something outside themselves rather than only from other symbols (Harnad, 1990). In generative AI, the term was adopted for tying a model's answers to verifiable sources, such as your documents or search results, which is what RAG does.
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