Real-World Case Studies

Theory is helpful. Results are better.

This section breaks down real-world examples—pages that succeeded in earning citations from LLMs, and pages that failed or were ignored. It includes concrete before-and-after rewrites, prompt testing outputs, and lessons learned from applying LLM SEO strategies in the field.

Case Study 1: A Page That Gets Cited — What It Did Right

URL: example.com/what-is-llm-seo Query Tested: “What is LLM SEO?” Tools: ChatGPT (Browsing), Perplexity, Claude

Citation Status:

• ✅ Cited by Perplexity
• ✅ Paraphrased by ChatGPT
• ✅ Direct match to Claude’s answer

Why It Worked:

• Clear answer at the top: First paragraph defined “LLM SEO” in a single declarative sentence.
• Semantic headers: Used <h1> for the title and <h2> for sub-questions like “Why LLM SEO Matters.”
• Structured summary block: TL;DR section present above the fold using an <aside> tag.
• Schema markup added: Included mainEntity with Question and Answer types in JSON-LD.

Example schema:

{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "headline": "What is LLM SEO?",
  "mainEntity": {
    "@type": "Question",
    "name": "What is LLM SEO?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "LLM SEO is the process of optimizing content to be surfaced and cited by AI models like ChatGPT, Claude, and Perplexity."
    }
  }
}

Prompt Test Output (Perplexity):

“LLM SEO is a methodology for making web content more likely to be cited or summarized in AI-generated answers. According to example.com, it focuses on structured content, semantic clarity, and crawler accessibility.”

Takeaway: Simple, direct language. Clean HTML. Schema. And the answer is at the top.

Case Study 2: A Page That Failed — What Was Missing

URL: example.com/seo-vs-ai-search-2023 Query Tested: “What is the difference between traditional SEO and LLM SEO?”

Citation Status:

• ❌ Not cited
• ❌ Not paraphrased
• ❌ Not linked

Why It Failed:

• Weak intro: Opened with 4 paragraphs of historical context before defining the core term.
• Vague headline and slug: “SEO vs AI Search 2023” didn’t match searcher or prompt intent clearly.
• No schema: No JSON-LD, no structured data—nothing to mark definitions or key sections.
• Content buried in a listicle format: Definitions hidden between images and long, speculative paragraphs.

Prompt Output (ChatGPT):

“Traditional SEO focuses on ranking in Google’s search engine result pages, while LLM SEO is concerned with being cited or summarized in AI-generated answers.”

Observation: None of this language appeared on the original page—it came from competitors or model training data.

Fix Strategy:

• Add structured summary at the top
• Include side-by-side bullet comparison of traditional SEO vs LLM SEO
• Rewrite title and headers for clarity
• Add FAQPage schema with relevant questions

Case Study 3: Rewrites That Improved Visibility

Before:

<h1>SEO and AI Content Discovery Trends</h1>
<p>The landscape of search has changed significantly over the last few years...</p>
<!-- No schema, no clear answer, no structured summary -->

After:

<h1>What Is AI Content Discovery?</h1>
<aside><strong>TL;DR:</strong> AI content discovery refers to how models like ChatGPT find, interpret, and reuse web content to answer user queries.</aside>
<p>AI content discovery is the process by which language models analyze online content for inclusion in generated answers. It differs from traditional search in that the content must be reusable, clear, and structurally sound for language synthesis.</p>

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "WebPage",
  "mainEntity": {
    "@type": "Question",
    "name": "What is AI content discovery?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "AI content discovery is how language models identify and reuse web content to generate answers, often without relying on traditional rankings or backlinks."
    }
  }
}
</script>

Result:

• After 3 weeks, the rewritten page was cited in Perplexity.
• ChatGPT began paraphrasing sections of the summary.
• Crawl logs showed ClaudeBot visited the page twice within 10 days of the update.

Case Study 4: Before/After Prompt Testing Examples

Prompt: “What is AI SEO?”

Before Rewrite Response (ChatGPT):

“AI SEO is a vague term. It may refer to optimizing AI-generated content or using AI for SEO tasks.”

After Rewrite Response:

“AI SEO is the practice of making content easier for language models like ChatGPT and Perplexity to understand and cite in answers.”

What changed:

• Page title was rewritten from “Using AI for Search Optimization” to “What Is AI SEO?”
• The first paragraph became a definition, not a thought piece.
• Added schema with mainEntity and acceptedAnswer
• Used prompt testing weekly and fine-tuned language to match phrasing that models tend to reuse

Strategic Commentary

Citations don’t happen by accident. Every successful case shares the same traits:

• A direct answer is visible at the top of the page
• Semantic clarity via headings, schema, and short paragraphs
• Schema provides machine-readable context
• TL;DR or definition box helps AI models extract key facts

On the other hand, the most common failures are always due to:

• Fluff intros
• No structured markup
• Generic content formats (e.g., long listicles or opinion posts)
• No testing or monitoring to know if changes made a difference

If you don’t test and track, you’re guessing. But with structured rewrites, prompt tests, and crawler logs, you can turn invisible pages into authoritative sources.

Next: [9. Building a Testing Dashboard for LLM SEO →]