AI 가 인용하는 콘텐츠를 만드는 5 가지 스키마 패턴 (코드 포함)

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is generative engine optimization?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Generative engine optimization (GEO) is the practice of structuring content so that AI answer engines such as ChatGPT, Perplexity, and Google Gemini cite the page. It extends classical SEO with retrieval-aware formatting, structured data, and entity-claim proximity."
      }
    },
    {
      "@type": "Question",
      "name": "Does FAQPage schema improve LLM citation rate?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes. Our Q1 2026 internal panel shows a 1.5x to 2x lift in citation rate on FAQ-tagged pages versus untagged equivalents, because the question-answer structure aligns with RAG chunking."
      }
    },
    {
      "@type": "Question",
      "name": "Can I add FAQPage schema to any page?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Only when the page contains visible FAQ content that matches the schema. Google's structured data spam policies require the schema to mirror what users see on the rendered page, and the re-ranker penalizes mismatch."
      }
    },
    {
      "@type": "Question",
      "name": "How many questions should a FAQPage block include?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Five to ten high-intent questions is the sweet spot. Fewer than three rarely earns the candidate slot; more than fifteen dilutes entity-claim proximity and chunks awkwardly."
      }
    },
    {
      "@type": "Question",
      "name": "Should every answer cite a source?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Statistical claims should. Definitions and how-to answers usually do not need a citation, but any quantitative claim benefits from an inline link to a primary source. Citations also raise re-ranker confidence in the answer block."
      }
    }
  ]
}

{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to add FAQPage schema to a Next.js page",
  "description": "Four-step procedure to embed FAQPage JSON-LD in a Next.js App Router page using a server component.",
  "totalTime": "PT15M",
  "supply": [
    { "@type": "HowToSupply", "name": "Next.js 14 or newer project" },
    { "@type": "HowToSupply", "name": "List of five to ten real FAQ questions and answers" }
  ],
  "tool": [
    { "@type": "HowToTool", "name": "Code editor" },
    { "@type": "HowToTool", "name": "Google Rich Results Test" }
  ],
  "step": [
    {
      "@type": "HowToStep",
      "position": 1,
      "name": "Collect questions from real user behavior",
      "text": "Pull the five to ten highest-volume questions from search console, support tickets, or sales-call transcripts. Each question must mirror something a user actually asks."
    },
    {
      "@type": "HowToStep",
      "position": 2,
      "name": "Write answers between 30 and 200 words",
      "text": "Each answer leads with the entity and the claim in one sentence, then expands with context. Avoid filler; the retriever discards padding."
    },
    {
      "@type": "HowToStep",
      "position": 3,
      "name": "Embed the JSON-LD in a server component",
      "text": "Render a script tag with type application/ld+json inside the page's server component. The JSON object must mirror the visible Q&A on the rendered page."
    },
    {
      "@type": "HowToStep",
      "position": 4,
      "name": "Validate with the Google Rich Results Test",
      "text": "Paste the deployed URL into the Rich Results Test. Resolve any warnings before publishing. Re-test after each content change."
    }
  ]
}

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "5 Schema Patterns That Get Your Content Cited by AI",
  "description": "Five JSON-LD schema patterns that lift LLM citation rate, with production-ready code examples.",
  "image": "https://promptarchitect.app/og/5-schema-patterns-llm-cited.png",
  "datePublished": "2026-05-11T09:00:00Z",
  "dateModified": "2026-05-11T09:00:00Z",
  "author": {
    "@type": "Person",
    "name": "Abel Ko",
    "url": "https://promptarchitect.app/authors/abel",
    "sameAs": [
      "https://www.linkedin.com/in/abelko",
      "https://twitter.com/livelikeabel"
    ],
    "jobTitle": "Founder, Prompt Architect"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Prompt Architect",
    "url": "https://promptarchitect.app",
    "logo": {
      "@type": "ImageObject",
      "url": "https://promptarchitect.app/logo.png"
    }
  },
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://promptarchitect.app/blog/5-schema-patterns-llm-cited"
  }
}

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "LLM Citation Rate Benchmark — 2026 Q1",
  "description": "Citation rate across ChatGPT, Perplexity, and Google Gemini measured over 1,000 commercial prompts in Q1 2026.",
  "url": "https://promptarchitect.app/research/citation-benchmark-2026-q1",
  "keywords": ["LLM citation", "GEO", "answer engine benchmark"],
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "creator": {
    "@type": "Organization",
    "name": "Prompt Architect",
    "url": "https://promptarchitect.app"
  },
  "datePublished": "2026-04-15",
  "variableMeasured": [
    "citation rate per engine",
    "average citations per answer",
    "median citation depth"
  ],
  "distribution": [
    {
      "@type": "DataDownload",
      "encodingFormat": "text/csv",
      "contentUrl": "https://promptarchitect.app/research/citation-benchmark-2026-q1.csv"
    }
  ]
}

{
  "@context": "https://schema.org",
  "@type": "ClaimReview",
  "datePublished": "2026-05-11",
  "url": "https://promptarchitect.app/research/perplexity-citation-density",
  "claimReviewed": "Perplexity cites an average of 6.2 sources per answer across commercial prompts.",
  "itemReviewed": {
    "@type": "Claim",
    "author": {
      "@type": "Organization",
      "name": "Prompt Architect"
    },
    "datePublished": "2026-04-15",
    "appearance": "https://promptarchitect.app/research/citation-benchmark-2026-q1"
  },
  "reviewRating": {
    "@type": "Rating",
    "ratingValue": 4,
    "bestRating": 5,
    "alternateName": "Mostly true",
    "description": "The 6.2 average holds for English-language commercial prompts. Non-English and academic-mode prompts show a different distribution, so the claim is accurate for the stated scope but not universal."
  },
  "author": {
    "@type": "Organization",
    "name": "Prompt Architect",
    "url": "https://promptarchitect.app"
  }
}