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Documentation Index

Fetch the complete documentation index at: https://wiki.jamesvarga.com/llms.txt

Use this file to discover all available pages before exploring further.

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Definition

Indexing for AI search means structuring content so that large language models and AI-powered retrieval systems can find, parse, and accurately cite it. Unlike traditional SEO, which optimises for keyword matching and link authority, AI indexing optimises for entity clarity, structural predictability, and extractable definitions.

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When it matters

It matters when a person or organisation wants their content to appear accurately in AI-generated answers, knowledge graphs, and agentic search results. As more information discovery moves through AI intermediaries rather than direct search, content that is not structured for machine readability becomes invisible.

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How it works

AI systems retrieve content by matching queries to chunks of text that contain clear entities, definitions, and factual claims. They prefer content with: a clear definition early in the document, stable and predictable headings, short sections, explicit internal links, and factual language rather than promotional or vague language. The key mechanisms are: entity identification (who, what, where, when), structural consistency (same heading patterns across similar pages), and link density (linking related concepts and pages explicitly).

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Practical steps

  1. Define the primary entity on every page within the first two paragraphs.
  2. Use consistent heading patterns across all similar page types.
  3. Keep sections short — one concept per section.
  4. Write definitions that can be extracted in under 10 seconds.
  5. Use explicit internal links with descriptive anchor text.
  6. Include a Related pages section on every page with a one-line reason for each link.
  7. Avoid marketing language and vague claims — prefer specific mechanisms and steps.

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Examples

This wiki is structured for AI indexing. Each knowledge page includes a Definition section, a When it matters section, and a Key takeaways section — all designed to be extracted cleanly by an LLM parsing the page.

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Common mistakes

  • Writing for human persuasion rather than machine extraction.
  • Using inconsistent heading patterns across similar content types.
  • Burying the definition of the subject deep in the page.
  • Using internal links without descriptive anchor text.

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Key takeaways

AI-indexed content is not a future consideration — it is a current discoverability requirement. The content that performs best in AI search is content that makes it easy for a machine to extract a clear, factual, specific answer.
Last modified on March 12, 2026