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What is extractability?

How easily an LLM can lift a clean, summarisable answer from your page. High extractability dramatically increases citation rate on grounded surfaces.

Definition

Extractable pages answer the question in the first 200 words, use question-style H2s, bullet-pointed conclusions, FAQ schema, and clean HTML. Long preambles, gated content, and JS-heavy rendering all suppress extractability.

Why it matters

Extractability sits in the "Signals & ranking" layer of the AI search stack. Teams that handle it well get cited more, recommended more, and earn more of the AI-mediated revenue in their category. Teams that ignore it spend a year wondering why their content investment never moves the needle inside ChatGPT or Perplexity.

Related terms

  • AEO - Optimizing content so answer engines (Perplexity, Google AI Overviews, ChatGPT Search) quote it as the direct answer to a query.
  • Schema (structured data) - JSON-LD or microdata that labels your content for machines. FAQ, Article, Product, HowTo, and Organization schema all improve LLM extractability.
  • AI Overviews - Google's generative-AI answer box at the top of search results, powered by Gemini. Now appears for more than half of qualifying US queries.
  • llms.txt - A proposed standard file at the root of a site that tells LLM crawlers what content is available, in what format, and how to use it.

Apply it

The LLM SEO playbook ties every concept in this glossary into a single operating model. If you want to see how your brand performs across all the LLMs at once - mention rate, citation share, sentiment, rank - start with the free GEO audit or skip straight to a free Livesov account.

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