Evidence GEO & AI Search

AI answers are grounded in retrieved source documents

The foundational RAG paper shows language models producing answers conditioned on documents retrieved at query time.

ID
SS-EV-028
Confidence
High · 88
Evidence
Strong
Updated
2026-07-08

The claim

To be cited by AI answer engines, content must be retrievable and quotable at the moment a question is asked.

What the evidence shows

Lewis et al. introduced retrieval-augmented generation, where a model retrieves relevant passages from a corpus and conditions its output on them. This architecture underpins how modern AI answer engines ground responses and attribute sources. Content that is easily retrieved and clearly states answers is far more likely to be surfaced and cited.

Source

Source
Lewis et al., 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks'
Type
Academic research
Year
2020
Strength
Strong

How SearchScore applies it

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