JavaScript-based format for implementing structured data, recommended by Google for embedding schema markup in pages.
JSON-LD (JavaScript Object Notation for Linked Data) is a structured data format that uses JSON syntax to embed schema markup directly into web pages. Google officially recommends JSON-LD as the preferred method for implementing structured data because it separates markup from HTML content, making it easier to implement and maintain than alternatives like Microdata or RDFa.
Unlike inline markup methods, JSON-LD lives in a tag within the page's or , containing all structured data in a single, organized block. This approach allows developers and SEOs to manage schema markup independently from page content, reducing the risk of breaking existing HTML while enabling rich snippets and enhanced search features.
Why It Matters for AI SEO
JSON-LD has become increasingly critical as AI systems like Google's neural matching algorithms and featured snippet extraction rely heavily on structured data to understand page content. Search engines use JSON-LD to feed their knowledge graphs, which in turn power AI-driven search features like answer boxes, rich snippets, and Google's Search Generative Experience. Modern AI content generation tools increasingly output JSON-LD alongside content, recognizing that structured data helps search engines understand context and relationships between entities. When AI systems generate content about products, events, or organizations, properly implemented JSON-LD ensures that search engines can accurately interpret and display this information in enhanced search results.
How It Works
JSON-LD implementation begins with defining the schema type (Person, Product, Article, etc.) and including relevant properties within a script tag. For example, an article might include headline, author, datePublished, and publisher properties. The format follows Schema.org vocabulary, using @context to define the schema namespace and @type to specify the entity type.
Tools like Schema Pro and Google's Rich Results Test help validate JSON-LD implementation, while technical SEO platforms like Screaming Frog can crawl sites to identify missing or malformed structured data. Many content management systems now include built-in JSON-LD generators, and AI writing tools increasingly suggest appropriate schema markup based on content type and entity recognition.
Common Mistakes
The most frequent JSON-LD error involves mismatched information between the structured data and actual page content. Google requires that JSON-LD accurately reflects what users see on the page—marking up content that doesn't exist or contradicts visible information can result in rich snippet removal or manual actions. Additionally, many implementers incorrectly nest multiple schema types or fail to include required properties, causing validation failures that prevent enhanced search features from appearing.