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Retrieval and RAG

What is Machine readability?

How easily a system can interpret content and data

Machine readability depends on semantic HTML, stable structure, clear data and explicit relationships. It does not mean writing for robots or sacrificing the human experience.

What Machine readability 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

Use real HTML for headings, lists, tables and links; avoid hiding essential content in images or fragile scripts.

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

Machine readability 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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