Grounding

What is Grounding?

Grounding is the process by which an LLM anchors each claim in its answer to verifiable passages from external sources during answer generation. Instead of relying solely on the model's parametric memory, the synthesized sentences are tied back to documents retrieved at the retrieval stage.

Two goals:

• Reduce hallucination — patch in facts that are missing or outdated in the training data using external sources.
• Attach citable evidence — make it explicit where the source cards, footnotes, and inline links beside the answer come from.

How is it different from RAG?

The two concepts are close but emphasize different things.

RAG is closer to a system architecture; grounding is closer to a property of the answer's quality. A well-designed RAG produces strongly grounded answers.

How it works

1. User query → split into sub-queries (query fan-out)
2. Retrieval against external indexes and documents for each sub-query → hundreds of candidate pages
3. Ranking and quality signals select passages → loaded into the context window
4. LLM generates sentences anchored to those passages + renders citation cards ← this is the grounding step

Impact on GEO

• At the grounding stage the LLM favors information confirmed across multiple sources — claims that appear on a single page lose to claims with accumulated external citations.
• Paragraph self-containment is critical — a passage must stand on its own to survive as a grounding candidate.
• Schema markup and direct-answer paragraphs help the LLM quickly recognize material that is ready for grounding.
• Brand and author names must be explicit inside the paragraph so that grounded citations carry your brand through.

Related Terms

• RAG
• Passage
• Query Fan-out
• Context Window
• Answer-First Paragraph