$ ~/ym8 --define structured-data-for-ai
Structured Data for AI
definition
Structured Data for AI extends traditional schema markup to serve the needs of AI engines alongside search engines. While Schema.org markup has long been used for SEO (helping Google understand page content for rich snippets), its importance has increased with AI engines that rely on structured signals to comprehend content context, relationships, and authority.
The structured data ecosystem for AI includes several layers. Schema.org markup (JSON-LD format) provides content-level structure: Article, Product, FAQ, HowTo, DefinedTerm, Organization, and other types that help AI engines understand what each page contains. AI-specific files (llms.txt, llm-profile.json, .well-known/ai.txt) provide brand-level structure: who you are, what you do, and how you should be described.
For AI Overviews and Gemini, structured data directly influences whether your content is selected for citation. Google's AI systems use schema markup to understand content type, authorship, publication date, and topical relevance. Pages with rich structured data are more likely to be included in AI-generated summaries.
Implementing structured data for AI is a Technical AEO task that provides compounding benefits. Once in place, it enhances how AI engines process all of your content—not just the pages where the markup is implemented. It signals to AI systems that your site takes machine-readability seriously, potentially increasing trust and citation frequency.
why_it_matters
Structured data provides the machine-readable context that AI engines need to accurately understand, categorise, and cite your content. Without it, AI engines must infer context from unstructured text, increasing the risk of misinterpretation or omission. Implementing structured data is one of the highest-ROI Technical AEO activities.
examples
- Adding DefinedTerm schema to glossary pages so AI engines understand they contain authoritative definitions
- Implementing FAQ schema that enables direct extraction by ChatGPT and AI Overviews
- Creating llm-profile.json with structured brand information for AI crawlers
faq
What schema types are most important for AEO?
The most impactful schema types for AEO are: Article (for blog posts and guides), FAQ (for question-answer content), HowTo (for process descriptions), Product (for product pages), Organization/Person (for brand identity), DefinedTerm (for glossary entries), and BreadcrumbList (for site hierarchy). Implement the types that match your content.
Should I use JSON-LD or microdata for structured data?
JSON-LD is strongly recommended. It is Google's preferred format, easier to implement and maintain, and works well with both search engines and AI engines. Place JSON-LD scripts in the <head> or body of your HTML pages.
Related Terms
Technical AEO
Technical AEO encompasses the infrastructure and technical configurations that help AI engines discover, crawl, parse, and cite your content. It includes AI-specific crawl policies, structured data implementation, llms.txt files, site architecture optimisation, and content formatting for AI consumption.
llms.txt
llms.txt is a plain-text file placed at a website's root that provides structured, machine-readable information about a brand, product, or organisation specifically for consumption by large language models. It functions as a "robots.txt for AI" — telling AI crawlers what your brand is and how it should be described.
llm-profile.json
llm-profile.json is a JSON-LD structured data file placed at .well-known/llm-profile.json that provides machine-readable brand identity, offerings, expertise, and preferred citation formats to AI crawlers and language models.
Content for AI
Content for AI refers to the practice of creating and structuring website content specifically to be effectively consumed, understood, and cited by AI engines. It involves answer-first formatting, clear factual claims, structured data, and comprehensive coverage of topics.
Related Engines
Monitor Your AI Visibility
See how your brand appears with the default core pair. Start with ChatGPT and Claude by default. Expand monitoring only when the workflow needs it.