What Vector database means in GEO
In a RAG pipeline, publishing is not enough. A page must be discoverable, enter the candidate set and survive reranking.
Why it matters
When building your own RAG, preserve source, date, language and URL metadata with every passage.
What to check in practice
Identify the stage where the source disappears: access, initial retrieval, reranking, generator context or citation selection.
How it connects to other concepts
Vector database belongs to retrieval and rag. It is best analyzed alongside related terms because AI visibility depends on several stages and signals rather than one isolated optimization.
Common mistake
Blaming final copy when the document never reached the model that writes the answer.
Turn these concepts into metrics
Bee LLM tracks mentions, citations, position, sentiment and competitors across major AI engines.
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