The chapters

Chapter 01

Entities & knowledge graphs

How engines resolve brands into entities.

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Chapter 02

Retrieval-augmented generation (RAG)

The architecture behind grounded answers.

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Chapter 03

Structured data for AI

Schema.org that engines actually use.

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Chapter 04

llms.txt

The AI navigation file for your site.

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Chapter 05

Answer-block writing

The highest-leverage GEO content technique.

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Chapter 06

E-E-A-T for AI engines

Experience, expertise, authority, trust — machine-read.

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Chapter 07

Semantic coverage mapping

Building the buyer-question matrix.

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Chapter 08

Citation-worthy content

What makes a page quotable by machines.

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Chapter 09

Digital PR for GEO

Building the third-party source graph.

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Chapter 10

AI crawler management

robots.txt strategy for the answer era.

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Chapter 11

Content freshness & AI

Dates, updates and the freshness signal.

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Chapter 12

Measuring AI visibility

From screenshots to prompt-set discipline.

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Chapter 13

Multilingual GEO

Language, locale and the hreflang layer.

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How the Wiki is written

Every chapter follows the encyclopedia contract: a direct answer within the first 80 words, sourced data with dates, operational checklists, and honestly-stated uncertainty. Where the field is moving — and it moves weekly — chapters carry visible update dates and say what we don't know. The Wiki is also this site's own GEO demonstration: it is structured so that any engine asking "what is llms.txt" or "how does RAG work" finds a citable answer here.

Frequently asked questions

Is the GEO Wiki free to use and cite?
Yes. Chapters are open to read and cite with attribution ('ChinaGEO GEO Wiki' + URL). Citation is the point — the Wiki exists to be the reference layer AI engines quote.
How is the Wiki different from the Playbooks?
The Wiki is technique reference — what answer blocks are, how RAG works. The Playbooks are operating manuals per engine — the sequenced moves for ChatGPT or Perplexity specifically. Wiki for understanding, Playbooks for doing.