Structured Data - LLM Guides

What is Structured Data?

Structured data is a machine-readable format that defines content elements for search engines and AI models. It provides clarity about your content by:

Defining the purpose and type of content
Improving citation and ranking in AI models
Providing relationship context between entities
Helping AI generate better answers using your data

Implementation Methods

There are three main ways to implement structured data:

JSON-LD (preferred by Google & LLMs)
Microdata
RDFa

Recommended: JSON-LD is clean, decoupled from your HTML, and easiest to maintain.

Common Schema Types

Most useful schema types for LLM visibility:

Article – For guides and blog posts
FAQPage – For question/answer sections
HowTo – For step-by-step instructions
Product – For tool and software listings
Organization – For establishing brand identity
VideoObject – For embedded YouTube/loom videos

Live JSON-LD Example


{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is structured data?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Structured data is metadata added to content that helps LLMs and search engines better understand it."
      }
    }
  ]
}

Best Practices

Use the most specific schema type possible
Always include author, headline, and datePublished
Validate schema with Google and Bing tools
Use canonical tags with consistent URLs
Update structured data when content changes

Test Your Structured Data

Pro Tip for LLM Optimization

LLMs like ChatGPT, Perplexity, and Claude can parse schema markup in real-time. This improves your chances of:

Being cited as an answer source
Appearing in Perplexity answer cards
Being used as a fallback trusted source by AI models

Track Performance

Use Google Search Console → Enhancements tab
Search your content on Perplexity to check visibility
Compare with ChatGPT's browser mode

Advanced Implementation Patterns

Complex scenarios often require combining multiple schema types. Here are some powerful patterns:

1. Article with Author and Organization

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Advanced LLM Optimization Techniques",
  "author": {
    "@type": "Person",
    "name": "Jane Smith",
    "jobTitle": "AI Research Director",
    "affiliation": {
      "@type": "Organization",
      "name": "Tech University",
      "url": "https://techuniversity.edu"
    }
  },
  "publisher": {
    "@type": "Organization",
    "name": "LLM Guides",
    "logo": {
      "@type": "ImageObject",
      "url": "https://llmlogs.com/logo.png"
    }
  },
  "datePublished": "2024-03-20",
  "dateModified": "2024-03-21"
}

2. HowTo with Video

{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "Implementing Structured Data for LLMs",
  "description": "Step-by-step guide to implementing structured data",
  "video": {
    "@type": "VideoObject",
    "name": "Structured Data Tutorial",
    "description": "Video tutorial on implementing structured data",
    "thumbnailUrl": "https://llmlogs.com/thumb.jpg",
    "uploadDate": "2024-03-20",
    "duration": "PT10M30S"
  },
  "step": [
    {
      "@type": "HowToStep",
      "name": "Choose Schema Type",
      "text": "Select the most specific schema type for your content"
    },
    {
      "@type": "HowToStep",
      "name": "Implement JSON-LD",
      "text": "Add JSON-LD script to your page"
    }
  ]
}

Real-World Use Cases

Technical Documentation

For API documentation and technical guides:

{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "headline": "LLM API Integration Guide",
  "author": {
    "@type": "Person",
    "name": "John Doe",
    "jobTitle": "Senior Developer"
  },
  "keywords": "LLM, API, integration, documentation",
  "articleSection": "API Documentation",
  "inLanguage": "en",
  "code": {
    "@type": "SoftwareSourceCode",
    "codeRepository": "https://github.com/example/llm-api",
    "programmingLanguage": "Python"
  }
}

Product Documentation

For software and tool documentation:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "LLM Optimization Tool",
  "description": "Tool for optimizing content for LLMs",
  "brand": {
    "@type": "Brand",
    "name": "LLM Guides"
  },
  "offers": {
    "@type": "Offer",
    "price": "99.99",
    "priceCurrency": "USD"
  },
  "documentation": {
    "@type": "TechArticle",
    "headline": "User Guide",
    "url": "https://llmlogs.com/docs"
  }
}

Dynamic Implementation

For content that changes frequently or is generated dynamically:

JavaScript Implementation

function generateStructuredData(content) {
  return {
    "@context": "https://schema.org",
    "@type": "Article",
    "headline": content.title,
    "author": {
      "@type": "Person",
      "name": content.author
    },
    "datePublished": content.publishDate,
    "dateModified": content.updateDate
  };
}

// Add to page
const script = document.createElement('script');
script.type = 'application/ld+json';
script.text = JSON.stringify(generateStructuredData(pageContent));
document.head.appendChild(script);

Server-Side Implementation

def generate_structured_data(article):
    return {
        "@context": "https://schema.org",
        "@type": "Article",
        "headline": article.title,
        "author": {
            "@type": "Person",
            "name": article.author.name,
            "jobTitle": article.author.title
        },
        "datePublished": article.publish_date.isoformat(),
        "dateModified": article.update_date.isoformat()
    }

# In your template
structured_data = generate_structured_data(article)
script_tag = f''

Monitoring and Maintenance

Keep your structured data effective with these practices:

Regular Validation: Check your structured data monthly
Performance Tracking: Monitor rich results in Search Console
Content Updates: Update structured data when content changes
Error Monitoring: Set up alerts for validation errors

Automated Testing Script

import requests
from bs4 import BeautifulSoup
import json

def validate_structured_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    scripts = soup.find_all('script', type='application/ld+json')
    
    for script in scripts:
        try:
            data = json.loads(script.string)
            # Validate against schema.org
            validation_url = f"https://validator.schema.org/validate?url={url}"
            validation = requests.get(validation_url)
            return validation.json()
        except json.JSONDecodeError:
            return {"error": "Invalid JSON-LD"}