Grounding

Grounding is the practice of anchoring a generative AI model’s answer in specific, verifiable information supplied at the time of the request, such as search results, a document set or a knowledge graph, instead of relying only on what the model memorised in training. A grounded answer can be checked against its sources, is more current, and is less likely to contain an AI hallucination. In AI search, grounding is the step where your content gets pulled in as evidence.

How it works

Google’s Gemini API documentation describes grounding with Google Search in stages: the model decides whether a search would improve the answer, generates and runs one or more queries, processes the results, and returns a response with citations linked to the parts of the text they support. Vertex AI offers other grounding sources too, including Google Maps, enterprise search and your own data through retrieval-augmented generation (RAG).

MLforSEO’s courses add the entity layer. Knowledge graphs store facts in a consistent structure, which makes them useful for grounding question-answering systems and for reducing hallucinations. Our posts describe AI Overviews and AI Mode cross-referencing entities against structured graphs to verify facts and disambiguate names before deciding what to include.

How it’s used in SEO and marketing

• Becoming a grounding source. Pages that are crawlable, indexed and easy to extract from (clear passages, specific claims) are more likely to be retrieved as evidence. See content chunking.
• Moving from grounding to citation. MLforSEO distinguishes the grounding pool from the citation set. Brands with weak or ambiguous entity signals often get used as context without being named, so entity disambiguation and consistent structured data matter.
• Grounding your own AI workflows. When you use an LLM for content briefs, summaries or classification, supplying source documents or search results reduces invented facts.
• Controlling use. Google notes that the Google-Extended token limits training and grounding in some of its other AI systems (outside Search).

Example

A user asks an AI search tool which running shoes suit flat feet. The system fans the question out, retrieves passages from several retailer and review pages, checks brand and product entities against its graph, and writes an answer grounded in those passages. A retailer whose page states arch support, drop and price in plain, specific sentences is used and credited. A page that says roughly the same as everyone else may be read, used for context and left uncited.

Limitations and things to watch

• Grounding reduces hallucinations but does not remove them. Models can still misread, merge or misattribute sources.
• The answer is only as good as what was retrieved. Outdated or low-quality sources produce confident, grounded mistakes.
• Each platform grounds differently, and most do not disclose which sources were considered but not cited.
• “Grounding” is also used loosely in our material in its everyday sense (a conceptual grounding). In AI search, it always means anchoring outputs to supplied evidence.

Learn it on MLforSEO

• What Is a Knowledge Graph? How to Use It for Smarter Keyword Strategy explains grounding and the citation gap.
• 5 Steps to Identify and Fix Entity Recognition Gaps That Block LLM Visibility covers why brands get used but not named.
• How modern search actually works: the five-stage retrieval funnel shows where retrieval and grounding happen.
• The AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands course teaches knowledge graphs as grounding infrastructure.