Structured data can clarify a page’s subject for systems that choose to use it, but it should never be treated as a shortcut to search placement. A publisher should first build an accurate HTML article, then add only the markup that actually describes it.
What JSON-LD is
JSON-LD is a JSON document that can encode structured vocabulary such as Schema.org Article, Organization or WebSite. Common keys include @context, @type and type-specific properties. A page can contain a JSON-LD script tag inside its HTML without needing a backend.
Begin with a simple, truthful example
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "How to Prepare Web Images",
"description": "A practical image preparation guide"
}This example is schematic, not a complete recipe for any particular rich-result feature. Real pages may need different required or recommended fields for different search features. Use a publisher’s real title and description; never make up reviews, authorship or organisation details.
Check the right kind of validity
Parsing JSON correctly is only step one. The next questions are whether the Schema.org terms make sense, whether properties are relevant and whether the structured content matches the page that visitors actually see. Search services may impose separate eligibility rules and do not guarantee rich results even for eligible markup.
Five common mistakes
- Copying script tags into a JSON-only parser instead of supplying just the JSON body.
- Using curly quotes, trailing commas or JavaScript comments in strict JSON.
- Marking up claims, ratings or author names that are not on the page.
- Assuming every valid Schema.org type triggers a special search display.
- Deploying markup that no longer matches an edited page.
A reliable editorial workflow
Proofread visible copy, prepare the JSON-LD, inspect syntax with our browser-only JSON-LD inspector, and where relevant test the published URL with official search documentation and tools. Review the page again after editorial changes.
Google’s structured data documentation explains the difference between adding data and meeting feature-specific requirements.