Why is JSON-LD (Schema.org) better than microdata (itemprop) from a GEO/AI perspective?
- Better consumption by models and crawlers - JSON is native format for parsers/LLM; minimizes DOM "noise" and microdata nesting errors
- Knowledge graph instead of DOM tree - in JSON-LD you have @id, entity relationships, de-duplication and linking (sameAs, @graph). Microdata does not offer this explicitly
- Easier maintenance - you change the content of the page without the risk of "spilling" the itemprop attributes; you can generate data from the feed (PIM/ERP) and inject it into the
<head> - Richer entity differentiation - types, identifiers (GTIN, MPN), variants, policies, coverage, languages; this is crucial for a correct LLM (hallucination reduction) response
- Multilingualism and context - easier to indicate inLanguage, alternative names, regions (areaServed) and map this to cross-border markets
- Standardization and compliance - search engines have preferred JSON-LD for years; many tools (validators, pipelines) are under this format
- Modularity under GEO - you can publish additional entities (FAQ, HowTo, Review, Organization, Offer, MerchantReturnPolicy) without changing the HTML
What does this mean in practice?
Use Schema.org as the dictionary and JSON-LD as the carrier. Microdata with itemprop only makes sense if you don't have access to the <head> and you have to "impersonate" existing HTML - but under GEO it's an inferior solution (fragile and semantically poorer).
Comparison
Microdata (abbreviated):
<span itemprop="name">SportFuel Isotonic</span>
<img itemprop="image" src="/img/isotonic.jpg" alt="SportFuel Isotonic" />
<span itemprop="gtin13">5901234567890</span>
<div itemprop="offers" itemscope itemtype="https://schema.org/Offer">
<meta itemprop="priceCurrency" content="EUR" />
<span itemprop="price">6.51</span>
<link itemprop="availability" href="https://schema.org/InStock" />
</div>JSON-LD (recommended under GEO/AI):
{
"@context": "https://schema.org",
"@type": "Product",
"@id": "https://example.com/p/sportfuel-isotonic#product",
"name": "SportFuel Isotonic",
"image": ["https://example.com/img/isotonic.jpg"],
"gtin13": "5901234567890",
"mpn": "SF-ISO-7",
"brand": { "@type": "Brand", "name": "SportFuel" },
"inLanguage": "pl",
"description": "Izotonik w saszetkach dla biegaczy i HYROX.",
"category": "Sports Nutrition",
"audience": { "@type": "PeopleAudience", "audienceType": "Endurance athletes" },
"offers": {
"@type": "Offer",
"url": "https://example.com/p/sportfuel-isotonic",
"priceCurrency": "EUR",
"price": "6,6",
"availability": "https://schema.org/InStock",
"hasMerchantReturnPolicy": {
"@type": "MerchantReturnPolicy",
"applicableCountry": "PL",
"returnPolicyCategory": "https://schema.org/MerchantReturnFiniteReturnWindow",
"merchantReturnDays": 14
},
"shippingDetails": {
"@type": "OfferShippingDetails",
"shippingDestination": { "@type": "DefinedRegion", "addressCountry": ["PL","DE","CZ"] },
"deliveryTime": { "@type": "ShippingDeliveryTime", "handlingTime": {"@type":"QuantitativeValue","minValue":0,"maxValue":1,"unitCode":"d"} }
}
},
"sameAs": [
"https://www.instagram.com/sportfuel",
"https://www.wikidata.org/entity/QXXXX"
]
}What to look out for "under GEO for AI"?
- Unambiguity:
@id,gtin*,mpn,brand,sku - Outreach and language:
inLanguage,areaServed,availableLanguage - Offer and logistics:
Offer,OfferShippingDetails,MerchantReturnPolicy,priceValidUntil,availability - Relationships: link entities (
Organizationowner,WebSite,BreadcrumbList) - Content to support LLM responses:
FAQPage,HowTo,Review,AggregateRating- help models create complete and reliable answers
Summary
In summary, Schema.org's JSON-LD format is today's top choice for visibility into AI and GEO strategies. It enables clean, unambiguous data structures that language models can easily interpret and combine with other sources. Microdata with itemprop only works well for simple cases, but doesn't offer the flexibility, scalability or contextual depth needed in a modern AI ecosystem.
FAQ
Are Schema.org and itemprop the same thing?
No. Schema.org is a dictionary of concepts (data types), and itemprop is an attribute used in microdata to embed them in HTML.
Why is JSON-LD better than microdata?
Because it separates data from content, is easier to maintain and better understood by AI and search engines.
Does AI actually use Schema.org data?
Yes. Language models (e.g. ChatGPT, Gemini, Perplexity) analyze JSON-LD structures to create more accurate answers.
Is it worth using microdata with itemprop?
Only if you don't have access to the section <head> pages. In any other case, it is better to use Schema.org in JSON-LD.
How does Schema.org affect GEO (Generative Engine Optimization)?
By clearly describing products, services and content, it helps AI correctly interpret data and increases brand visibility in generative responses.
Glossary
Schema.org - an open dictionary of terms used to describe data on websites in a way that search engines and AI can understand
JSON-LD - jSON-based structured data storage format, recommended by Google and best processed by AI models
Microdata - older way of embedding data in HTML using attributes such as itemscope, itemtype, itemprop
itemprop - microdata attribute specifying a property (e.g., name, price, image) of an entity
GEO (Generative Engine Optimization) - optimizing content and data so that it is better understood and presented in results generated by AI models, such as ChatGPT or Gemini
Entity - a unit of knowledge (e.g., product, company, person) that can be uniquely described in the Schema.org structure
Knowledge Graph - a network of interrelated entities and their relationships, used by AI to better understand the context of data
Crawling AI - the process by which models or search engines process structured data to build their own internal knowledge bases
LLM (Large Language Model) - a large language model, such as ChatGPT or Gemini, that generates responses based on context and data from various sources
Offer / Product markup - schema.org data structure for describing products, their prices, availability and variants in e-commerce
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