Master Perplexity AISearch Optimization

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<img src="hero.webp" width="800" height="400" alt="Hero image" loading="eager">

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<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Your Article Title",
  "datePublished": "2025-01-01T00:00:00Z",
  "dateModified": "2025-01-15T10:30:00Z",
  "author": {
    "@type": "Person",
    "name": "Expert Author",
    "expertise": "AI and Machine Learning"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Your Publication"
  }
}
</script>

<main>
  <article>
    <header>
      <h1>Main Topic</h1>
      <p class="summary">Brief overview of key points</p>
    </header>
    <section>
      <h2>Key Insight #1</h2>
      <p>Detailed explanation with supporting evidence...</p>
      <aside class="related-info">
        <h3>Quick Fact</h3>
        <p>Statistical information or key takeaway</p>
      </aside>
    </section>
  </article>
</main>

<blockquote class="key-insight" cite="https://source-url.com">
  <p>"The primary factor in Perplexity optimization is content freshness, 
     accounting for 35% of ranking weight according to our analysis."</p>
  <footer>
    <cite>— Research Study, <time datetime="2025-01">January 2025</time></cite>
  </footer>
</blockquote>

<div class="stat-highlight">
  <span class="number">94%</span>
  <span class="description">of Perplexity responses include citations</span>
</div>