If you just want to remember one conclusion: if you want it to be read cleanly and referenced by the AI engine, use JSON-LD. Replacing the same schema.org markup with Microdata or RDFa will not cause it to be rejected by Google, but it will force the AI crawler to spend more effort to put the attributes scattered throughout the HTML back together into one entity. Every extra step is an opportunity to be missed or misread.
The three formats actually describe the same set of vocabulary
Let’s first clarify a point that is often confused: JSON-LD, Microdata, and RDFa are not three competing materials, but three syntaxes for writing schema.org vocabulary into web pages. The entities you want to mark are all the same. Organization, Product, Article, FAQPage, and HowTo are all the same vocabulary. The only difference lies in where these attributes are placed and in what form they are carried. Schema.org officially supports all three, and Google can also parse all three. Therefore, the comparison in this article is not "who can use it", but "which one makes it easiest for the machine to read".
Fundamental difference: How structured data is coupled to HTML
The most critical difference between the three is how tightly structured data is tied to HTML. Microdata writes tags directly into HTML elements, relying on the attributes itemscope, itemtype, and itemprop to hang them on div, span, and h1 layer by layer. RDFa follows the same idea and instead uses a set of attributes such as vocab, typeof, and property to stack semantics in existing tags, making the expression more complete and the grammar more verbose. JSON-LD is completely the opposite. It writes the entire entity as an independent JSON and puts it in a script tag with type application/ld+json, which is separate from the HTML on the screen.
- JSON-LD: A self-contained JSON object, concentrated in the script tag; decoupled from the layout, it is best maintained and programmed, and it is also the clear first choice of Google.
- Microdata: Use attributes such as itemscope and itemprop to embed them in visible HTML; the tags are tied to the content, and it is easy to damage the tags when changing the layout.
- RDFa: Embedded with vocab, typeof, and property attributes, it has the strongest semantic expression and is closest to W3C's link data ideal, but it has the heaviest syntax and is the easiest to make mistakes.
Why AI crawlers prefer JSON-LD
The reason why AI crawlers save trouble when reading JSON-LD is very straightforward: an entity is a complete JSON object, with fields, values, and nested relationships all in the same block. When the parser reads it in, it becomes a clean object tree, and there is no need to go back and scan the entire DOM. Microdata and RDFa require the machine to first visit the entire HTML, collect the attributes scattered on different tags one by one, and then reorganize them into an entity according to the nested relationship. The more complex the page and the deeper the layout layers, the higher the chance of errors during reorganization.
Two other practical factors magnify the gap. The first is JavaScript rendering: many AI crawlers execute JS more conservatively than Googlebot. If your Microdata is dynamically inserted into the DOM by the front-end framework, it may not be generated at the moment of crawling. The common practice of JSON-LD is to output the complete string on the server side, and it can be used as soon as it is captured. The second is the maintenance cost: JSON-LD is concentrated in one place. If you want to add a new FAQPage or change the product price, you only need to change a piece of JSON, which does not affect the layout; for inline tags, you have to re-confirm whether the attributes have been moved or deleted every time it is revised.
Do Microdata and RDFa still exist?
This doesn’t mean Microdata and RDFa are wrong. If you are using some old CMS or e-commerce theme, the built-in structured markup is probably Microdata, and there is no need to change it if it works properly. RDFa is still valuable in scenarios where complex linked data need to be expressed across vocabularies, such as open government data or academic publishing. But as far as the specific goal of "being referenced by AI engines" is concerned, the strengths of the two are almost useless, and the shortcomings will be magnified.
Migrate to JSON-LD, avoid these pitfalls first
- The mark is inconsistent with the content on the screen: the rating, price, and author written in JSON-LD must match the content actually seen by the user, otherwise it may be marked as spam.
- There is script but required fields are missing: For example, if Product is missing name or offers, FAQPage questions do not have corresponding answers, the machine will directly skip the entire entity.
- Forget to use @id to connect entities: When Organization, WebSite, and Article are related to each other, using @id to establish references allows AI to build a more complete brand knowledge map.
- Go live without validation: Running it through Google's Rich Results Test or schema.org validator is much faster than finding errors later from the search console.
An important point that is easily overlooked: the purpose of structured data is not to fool the machine, but to help the machine confirm what it reads from the screen. Therefore, the most stable approach is to make JSON-LD a mirror of the visible content. Only what is on the screen is written into the mark. Everything declared in the mark can be found on the screen. This consistency is the underlying basis for the AI engine to judge whether a page is trustworthy and worth citing.
Structured data does not make a page with empty content worth citing; its real role is to prevent a page that should be cited from being skipped because it is incomprehensible to machines.— Tenten GEO Technical Audit Team
How to check your website
Back to your own website, you can ask three questions first: What format is used for the main page? Has the same entity been tagged repeatedly? Do the JSON-LD fields match the screen content? The structured data of most B2B SaaS websites has been accumulated through different periods and outsourcing. It is common to have mixed formats and incomplete fields. In the eyes of AI engines, these gaps are reasons to skip you. If you want to know what your website looks like in the eyes of AI crawlers and which entities are missed, you can make an appointment for a 30-minute GEO diagnosis, and we will directly take your actual page and go through it.
