Structured data in SEO is a standardised way of providing information about the contents of a webpage in a format machines can easily understand. While much of this information is already visible on your site, structured data ensures that browsers, crawlers, and AI systems can interpret it clearly.

Why it matters today:

  • Rich results: JSON-LD markup helps your site qualify for enhanced snippets in Google.
  • Knowledge Graph: It connects your brand and content to entities recognised by Google.
  • AI search: Structured data now plays a role in visibility within AI Overviews and other generative search features.

This guide is designed to modernise your understanding of JSON-LD, show you how to write it correctly, and introduce how JSON is also used in AI prompt design.

Schema/JSON-LD Key Terms

  • Key-value pair: A data unit with a key (name) and value (content).
  • Property: A defined key in schema.org (e.g., name, address).
  • Object: A group of related key-value pairs describing one entity.
  • Array: A list of multiple values or objects for a single property.
  • Nesting: Placing one object inside another for more detail.
  • Parsing: The process of machines reading and interpreting JSON-LD.
  • @context: Declares the vocabulary being used (usually https://schema.org).
  • @type: Defines what type of entity is being described (e.g., Product).
  • Syntax: The rules governing structure, punctuation, and formatting.
  • Validation: Checking schema against rules and tools to ensure it is correct.

JSON vs JSON-LD vs Microdata vs RDFa

Structured data can be written in several different ways, but the one you’ll use most as an SEO is JSON-LD. JSON-LD stands for JavaScript Object Notation for Linked Data, and it is Google’s preferred format for implementing structured data. It sits inside a <script type=”application/ld+json”> block, meaning it is separate from your HTML tags and doesn’t interfere with visible page content. This makes it much easier to manage, update, and validate.

By contrast, Microdata and RDFa use attributes within existing HTML tags. For example, you might wrap your product name in a span tag with additional attributes that define it as a property. While this works, it mixes human-readable content with machine-readable instructions, which quickly becomes messy and hard to maintain at scale. JSON-LD avoids this by keeping structured data completely separate from the page’s visual presentation.

In practice, unless you’re working with an older CMS or a legacy system, you should always choose JSON-LD. It is cleaner, easier, and far more widely supported by modern search engines.

Why Use Structured Data?

Structured data is not just technical decoration, it is a communication layer between your website and search engines. By marking up your content in a way that machines can easily interpret, you help Google and other platforms understand what your page is about.

One of the biggest benefits is eligibility for rich results. These are the enhanced search snippets you see in Google with star ratings, event details, or FAQs. A simple Review or AggregateRating schema can display visual stars under your listing, which can improve click-through rates dramatically. But the benefits extend beyond visuals. Structured data also improves how your content is connected to entities in the Knowledge Graph, strengthening your brand’s presence online.

For SEOs, the ability to write and validate structured data is a fundamental skill. It’s not only about rankings, it’s about maximising visibility and clarity in a search landscape that is increasingly AI-driven.

Core JSON-LD Concepts

Before you start writing schema, it’s important to understand the building blocks that make up JSON-LD.

Key-value pairs are the simplest element. In JSON-LD, data is expressed as pairs of a “key” and a “value.” For example, “name”: “Lucy’s Cake Shop” has name as the key and Lucy’s Cake Shop as the value. Keys are always strings, and values are often strings too, though they can also be numbers, objects, or arrays.

Properties are specific types of key-value pairs defined by schema.org. Each schema type has required and recommended properties. For instance, a LocalBusiness schema requires both name and address. Properties ensure consistency so that all businesses use the same terms for common attributes.

Objects group related properties together. For example, an address might be defined as its own object using the PostalAddress schema, with nested properties like streetAddress and postalCode. Objects allow you to describe entities in more detail.

Arrays are used when a property has multiple values. For example, if your business opens on several different days, you can list them in an array like [“Monday”, “Tuesday”, “Wednesday”]. Arrays often contain multiple objects, such as several OpeningHoursSpecification blocks.

The key point: JSON-LD is flexible enough to describe simple values and complex nested structures, which is why it’s so powerful for SEO.

JSON-LD Syntax Basics

Implementing JSON-LD requires following a few syntax rules. Schema must be wrapped in a <script type=”application/ld+json”> tag within your HTML, either in the head or body.

Inside, curly braces {} contain your schema object. Each property is written as a key-value pair, with quotation marks around both the key and the value. Colons : separate the key from the value, and commas , separate different properties.

For example:

<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Example Ltd",
"url": "https://www.example.com"
}
</script>

The @context property tells search engines which vocabulary you are using, in almost all cases, this will be https://schema.org. The @type property specifies the type of entity, such as Organization, Product, or Article.

Nesting is required when a property itself needs to be defined with its own set of attributes. For example, address is not just a string; it’s a PostalAddress object with its own keys and values. Arrays are needed when multiple objects or values are attached to the same property, such as multiple opening times.

The syntax must be precise. A single missing comma or incorrect quotation mark will break the schema. That’s why validation (covered later) is so important.

Validating and Troubleshooting

Validation is an essential step in implementing structured data. Even a small syntax error can cause your schema to break.

The two most important tools are:

  • Google Rich Results Test: Tests whether your schema is eligible for rich results and flags any missing required or recommended properties.
  • org Validator: Provides a more general syntax check and highlights errors or warnings.

Common problems include:

  • Syntax errors: Usually missing commas, colons, or braces. Validators will point you to the exact line.
  • Invalid properties: Adding a property that doesn’t belong to that schema type.
  • Missing fields: Forgetting to include required properties like name or address.

Troubleshooting means comparing your schema against the official documentation and making sure your markup matches the content on your page. Schema that doesn’t reflect visible content can be considered spammy and ignored.

JSON and AI Prompts

JSON has become a central format not only for structured data, but also for working with AI systems. Large language models like ChatGPT, Gemini, and Claude often accept and return JSON-formatted instructions. The structured approach of JSON reduces ambiguity and ensures predictable outputs.

For example, you could design a JSON prompt like this:

{
"task": "Write meta description",
"input": "Product: Stainless Steel Water Bottle, 750ml",
"constraints": {
"length": "155 characters max",
"style": "informative, persuasive"
},
"output": "string"
}

This prompt tells the AI exactly what is required, what constraints to follow, and what format to return the output in. SEOs can use this technique to scale content creation workflows, from writing meta descriptions to generating FAQs, while maintaining consistency and accuracy.

The overlap between JSON-LD and AI JSON prompts is clear: both are about giving machines structured, unambiguous data to work with.

Advanced Uses

JSON-LD has evolved beyond being just a tool for rich snippets. Its role is central to how search engines and AI interpret the web.

Entity-first SEO. Modern SEO is about entities, not just keywords. JSON-LD helps connect your content to recognised entities by using properties like sameAs with authoritative links to Wikidata, LinkedIn, or official social profiles. This builds stronger connections in Google’s Knowledge Graph, making your brand more likely to appear in entity-based search results and AI answers.

Multimodal schema. JSON-LD is also expanding into describing non-textual content. You can now use schema to mark up podcasts, videos, images, and even datasets. This structured information helps AI systems surface your content in multimodal search environments, where users may ask questions that blend text, images, and video.

In short, JSON-LD is no longer optional. It’s a foundation for how your content is understood and surfaced across both traditional search results and AI-driven discovery systems.

Conclusion

JSON-LD remains the simplest, most effective way to add structured data to your site. It helps win rich results, builds connections to entities, and ensures your site is ready for AI-driven search experiences. Start with basic schemas, validate carefully, and then scale across your site. In 2025, structured data is not only about SEO, it’s also about bridging your content to AI systems.