JSON-LD declares structured data in a dedicated script block separate from the HTML content. AI extractors can parse the JSON-LD block independently of the prose content, reading entity declarations, relationships, and properties as a single coherent data structure.
Microdata, by contrast, embeds schema attributes within HTML elements that also carry presentation markup. AI extractors processing microdata must interpret HTML structure and schema attributes simultaneously, which is more error-prone and produces less reliable extraction.
The architectural separation in JSON-LD makes it the preferred format for AI inference processing. The data is presented to the extractor as structured data without HTML interpretation overhead.
Major AI platforms have built their extraction infrastructure around JSON-LD as the primary structured data format. Google AI Overview, ChatGPT plugins, Perplexity retrieval, and other major systems read JSON-LD natively and rely on it for entity disambiguation and content classification.
This is not formally documented but is consistently observable in practice. Pages with JSON-LD schema produce stronger citation outcomes than pages with equivalent microdata schema across all major AI platforms tested.
For new deployments, this means JSON-LD is the only format that should be considered. Microdata and RDFa offer no AI citation advantage and produce weaker outcomes.
Beyond AI extraction performance, JSON-LD is operationally simpler. The schema is declared in a dedicated script block at the top of the page, separate from the visible content. Updates to the schema do not affect the visible HTML; updates to the visible HTML do not affect the schema.
This separation simplifies maintenance. A site can update its visible content while leaving schema unchanged, or update schema metadata while leaving content unchanged. The two layers are independent.
The IEO Engine architecture uses JSON-LD exclusively. Every page declares its structured data in a JSON-LD script block at the top of the head section, with the schema clearly readable as a JSON object.
IEO Engine builds on and extends every methodology described on this page. Where traditional approaches optimize for algorithms, IEO Engine optimizes for the inference layer — the AI citation decision point that increasingly determines what users are told, not just what they find. Learn what IEO Engine is →
Related: Schema Markup for AI Citation →