Grounding
Also called Grounded generation, Search grounding
Tying an AI model's answer to specific, checkable sources, such as search results or your documents, instead of its memory alone.
What is grounding?
Grounding means connecting an AI model's answer to real sources of information. Instead of answering only from what it absorbed during training, a grounded model is given relevant material, such as web search results, company documents or database records, and builds its answer from that, usually with citations you can follow.
How does it work?
- The system fetches information related to the question: a web search, a RAG lookup in your documents, or a tool call to a live system.
- That material is added to the model's context with an instruction to answer from it.
- The answer links each claim back to the source it came from.
Many AI assistants and model APIs offer search grounding as a setting you can switch on.
What is it commonly confused with?
- A guarantee of accuracy. Grounding greatly reduces made-up facts (hallucinations), but the model can still misread a source, rely on a poor one, or overstate what it says. Citations make checking easier; they don't replace it.
- RAG. RAG is one way to ground a model. Grounding is the broader goal of anchoring answers in sources, whether those come from search, documents or tools.
Why does it matter?
Grounding is what makes AI answers current and checkable. Without it, a model can only offer what it remembers from training, which may be out of date or wrong, and it gives you no way to verify it. When you compare AI tools, check whether answers are grounded and whether the citations point to the exact passage. Read more in Can you trust AI-generated citations?
