Glossary

What is Grounding?

Connecting a language model's answer to retrieved real-world sources at answer time, reducing hallucination and creating the citation opportunity.

Definition

Grounding is the technique of anchoring a model's output to retrieved evidence — live web pages, documents, or a search index — rather than letting it answer purely from training memory. Grounded answers quote and cite; ungrounded answers reconstruct from statistical memory and can hallucinate.

Google explicitly calls this “grounding with Google Search” in Gemini; ChatGPT Search and Perplexity are grounded by design.

Why it matters

Grounding is the mechanism that makes GEO actionable in real time. Training-data visibility takes months and model releases to change; grounded retrieval reads your page today. A page published this week can be cited this week if it's crawlable and answers the prompt. It also splits your strategy in two: be present in training data (long game) and win retrieval (fast game).

Frequently asked

How do I know if an answer about my brand was grounded?

Citations are the tell. If the assistant links sources, retrieval happened; if it answers from memory with no links, you're seeing training-data knowledge — accurate only as of the model's cutoff.

Does grounding eliminate hallucination?

Reduces, not eliminates. Models can still misread retrieved pages or blend memory with evidence — which is why monitoring what assistants actually say about you matters.

Related terms