Structured Data for AI

Structured data for AI refers to schema and markup practices—like FAQPage, HowTo, Product, Organization—that clarify content types and relationships for AI systems and answer engines. While schema alone won’t guarantee citations, it helps AIs parse your content and align snippets with user questions.

Why schema helps

  • Clarity: Identifies entities, properties, and page purpose.
  • Extractability: Reinforces question‑answer and step formats.
  • Consistency: Reduces ambiguity across related pages and templates.

Useful schema types

  • FAQPage: Clear Q&A pairs that align with AI training patterns.
  • HowTo: Step‑by‑step instructions with materials and times.
  • Product/Review: Price, ratings, features, and pros/cons.
  • Organization/Person: Provenance and E‑E‑A‑T signals.

Implementation tips

  • Mirror visible content: Schema should match what’s on the page.
  • Keep it current: Update prices, dates, and versions.
  • Validate: Use testing tools; keep markup consistent across templates.

Measuring impact

How we implement and validate

We start with the content. Markup should mirror what is visible to users. On FAQ pages we ensure each question has a short, direct answer and then add FAQPage schema. For tutorials we structure clear steps and materials and use HowTo. For products we standardize price, features, and reviews. We validate with testing tools and spot check live pages after releases. We also keep schema consistent across templates so crawlers see the same patterns everywhere.

How our product helps

We track whether marked up pages appear more often in citations and whether mentions rise for prompts tied to those pages. We watch position as well as count. When a schema change correlates with better extractability or attribution, we replicate it across similar templates and annotate the change on our dashboard timeline.

References

Related concepts

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