Grounding
Grounding is the technique where an AI model anchors its answer in verifiable external sources, so facts are checkable and hallucinations are prevented.
Grounding is the umbrella term for techniques that link an LLM to trusted external information during answer generation. The best-known implementation is RAG, where a search index supplies the facts. Other forms: knowledge-base lookups, tool calls (APIs), and structured data queries. For GEO, grounding explains why your page can appear in an AI answer at all. A non-grounded LLM reproduces only training data; a grounded LLM retrieves current information and cites sources. Optimising for GEO = optimising for the grounding step.
Example
You ask Google AI Mode 'what is the current Bitcoin price?'. Without grounding the model would guess from training data. With grounding, the engine calls a crypto-price API, gets the current price, and generates an answer with that real value plus a citation.
Frequently asked questions
Is grounding the same as RAG?
RAG is one form of grounding — the most common in AI search engines. Grounding is broader: it also includes structured-data lookups, knowledge-graph queries, and tool use.
What GEO factors help my page get chosen as a grounding source?
Crawlability (retrieval), clear h2/h3 structure (parseability), direct answers literally rephrasing the question, schema markup (machine comprehension), freshness, and domain authority.
Related terms
Further reading
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