AXD for Content Teams

Adopt Markdown as your primary content format for agent-facing pages - it is human-readable, machine-parseable, lightweight for context windows, and structured enough for reliable extraction.

Structure content with clear hierarchical headings, semantic sections, and explicit metadata - agents parse structure, not visual layout.

Write content that answers questions directly in the first paragraph - agents extract answers from content, they do not read for pleasure.

Include structured data alongside narrative content - JSON-LD, Schema.org markup, and explicit attribute-value pairs that agents can parse without inference.

Design content for extraction, not just consumption - every page should contain at least one standalone, quotable definition or fact that agents can cite directly.

Publish an llms.txt file at your domain root that provides a structured overview of your content, expertise, and key pages for AI systems.

Create a comprehensive llms-full.txt with detailed definitions, FAQ pairs, topic clusters, and citation guidance for AI answer engines.

Implement FAQ schema (JSON-LD) on key pages - AI answer engines prioritise structured question-answer pairs for direct citation.

Use DefinedTermSet schema for glossaries and vocabulary pages - this tells AI systems that your definitions are authoritative and citable.

Ensure every page has a unique, descriptive meta title (30-60 characters) and meta description (50-160 characters) optimised for both search engines and AI extraction.

Write content that maps to user intents, not just topics - agents search for answers to specific questions, not general information on broad subjects.

Create content clusters around core concepts with clear internal linking - agents follow link structures to build comprehensive understanding of your expertise.

Use consistent terminology across all content - agents build entity models from your content, and inconsistent naming creates ambiguity.

Design content for comparison - agents evaluating your offerings against competitors need structured, comparable data points, not subjective claims.

Publish content that explicitly states what you do and do not offer - agents need clear boundaries to make accurate recommendations.

Write definitive, quotable statements that AI systems can extract and cite directly - 'X is Y' format definitions are the currency of AEO.

Structure content with explicit question-answer patterns - AI answer engines prioritise content that directly addresses common queries.

Build topical authority through comprehensive coverage - publish deeply on your core topics so AI systems recognise you as the authoritative source.

Implement Person schema markup for key authors and experts - AI systems attribute expertise to individuals, not just organisations.

Monitor AI citation patterns - track how AI systems reference your content and optimise for the formats and structures they prefer.