Grounding · Grounding with Search
Grounding is anchoring an LLM’s answer in verifiable web sources rather than in its parametric memory alone. It reduces hallucinations and determines whether citations appear: without a grounding step, a model does not cite a page.
Standalone definition · 36 words
How does grounding work?
The system decides whether the query needs recent or verifiable information, generates search queries, retrieves passages, then produces the answer based on them and returns the links. At its launch in the Gemini API, grounding with Google Search included a dynamic retrieval threshold: the model assesses whether search is useful before triggering it.
Grounding and RAG: what is the difference?
RAG is the general retrieve-then-generate architecture, usually applied to a controlled corpus such as internal documents. Grounding refers to the case where the source of truth is the live web. In both cases, retrieved passages are what produce citations.
How do you optimize for grounding?
Be indexed and retrievable, offer self-contained, precise passages, date time-sensitive information and name entities clearly. The grounding queries exposed by Bing show the phrasing used for retrieval.
Frequently asked questions
Do all AI engines ground?
Not systematically. Some models first assess whether a search is useful before running one.
Does grounding eliminate hallucinations?
It reduces them without eliminating them: the model can misread a source or cite an inaccurate one.
What are grounding queries?
They are the queries sent to the search index to retrieve sources. Bing exposes a sample.
Related terms
Further reading
- Profound vs Otterly vs Ahrefs Brand Radar: 2026 Query Fan-Out Comparison
- Microsoft Clarity, Bing Webmaster Tools: the Only GEO Stack That Actually Measures LLM Visibility
- Bing AI Performance : how to exploit the data for ranker on ChatGPT, Copilot and other LLM
- ← Back to the GEO glossary (114 terms)