Part of Generative Engine Optimization
JSON-LD Structured Data
JSON-LD is the schema.org format AI engines use to identify entities, articles, FAQs, products, and organizations on a page.
Overview
JSON-LD wraps a page's facts in a machine-readable envelope so search and AI engines don't have to guess. Organization schema clarifies who you are; Article schema clarifies what a piece of content is and who wrote it; FAQPage schema exposes Q&A pairs verbatim; Product schema exposes price and availability. Without JSON-LD, AI engines fall back to inference — and inference is where citations get attributed to your competitor.
Components
- Organization
Brand entity
- Article / BlogPosting
Content entity
- FAQPage
Q&A markup
- HowTo
Step-by-step markup
- Product
Commercial entity
Related entities
- schema.org — specification
- Speakable — voice extension
- JSON-LD generator — tool
Key facts
- JSON-LD is the format Google explicitly recommends over Microdata or RDFa.
- AI engines extract sameAs links to verify entity identity across the web.
- Invalid JSON-LD silently fails — always validate with the Rich Results Test.
How to apply JSON-LD Structured Data
- 1
Review Organization
Brand entity. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 2
Review Article / BlogPosting
Content entity. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 3
Review FAQPage
Q&A markup. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 4
Review HowTo
Step-by-step markup. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 5
Review Product
Commercial entity. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
Implementation checks
| Check | Pass condition | Evidence to keep |
|---|---|---|
| Accuracy | Every claim matches the visible page and current source material. | Source URL and review date |
| Delivery | Important content is present in the public server response. | Saved response or crawler test |
| Consistency | Names, URLs, dates, and definitions agree across related pages. | Template and entity audit |
| Measurement | A fixed test shows whether discovery or answer accuracy changed. | Baseline and follow-up results |
Common mistakes
- Treating JSON-LD Structured Data as a one-time task instead of maintained site infrastructure.
- Publishing claims or markup that visitors cannot verify on the page.
- Changing several variables at once, then guessing which change affected visibility.
- Measuring a single AI answer instead of a fixed panel of repeatable prompts.
Questions about JSON-LD Structured Data
- What is JSON-LD Structured Data?
- JSON-LD wraps a page's facts in a machine-readable envelope so search and AI engines don't have to guess. Organization schema clarifies who you are; Article schema clarifies what a piece of content is and who wrote it; FAQPage schema exposes Q&A pairs verbatim; Product schema exposes price and availability. Without JSON-LD, AI engines fall back to inference — and inference is where citations get attributed to your competitor.
- Where should implementation start?
- Start with Organization, verify the public result, then work through the remaining components in order. Record a baseline before making changes so the follow-up is meaningful.
- How should success be measured?
- Track technical validity, crawler access, accurate extraction, and citation presence across a fixed set of relevant questions. A single answer or crawler visit is not a trend.