Skill-Details
ai-search-optimization
Useful to web/SEO consultants, but specialized to AI search optimization.
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SKILL.md
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---
name: "ai-search-optimization"
description: "Optimizes content for Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), including schema, llms.txt, AI crawler rules, conversational query targeting, and visibility measurement. Use when the user asks about AI search visibility, ChatGPT Search, Perplexity, Google AI Overviews, AI crawlers, AEO, GEO, llms.txt, or making a site easier for answer engines to cite."
metadata:
version: "1.0.0"
origin: "webconsulting"
license: "MIT / CC-BY-SA-4.0"
---
# AI Search Optimization (AEO & GEO)
> Source: https://github.com/dirnbauer/webconsulting-skills
> **Scope:** Optimizing content for AI-powered search engines and answer engines
> This skill covers strategies for visibility in ChatGPT, Perplexity, Google AI Overviews, Microsoft Copilot, and other generative AI platforms.
## 1. Understanding AEO & GEO
### What is AEO (Answer Engine Optimization)?
Answer Engine Optimization focuses on structuring content to provide **direct, concise answers** to user queries through AI-powered platforms. Unlike traditional SEO which aims for link clicks, AEO optimizes for being **cited as the answer source**.
**Target platforms:**
- Google AI Overviews (formerly SGE)
- Perplexity AI
- ChatGPT Search
- Microsoft Copilot Search
- Voice assistants (Siri, Alexa, Google Assistant)
### What is GEO (Generative Engine Optimization)?
Generative Engine Optimization is the broader discipline of enhancing content visibility within **AI-generated search results**. It targets generative engines that synthesize answers from multiple sources rather than presenting traditional link lists.
**Key differences from traditional SEO:**
| Aspect | Traditional SEO | AEO/GEO |
|--------|----------------|---------|
| Goal | Rank in SERPs | Be cited in AI answers |
| User behavior | Click through to site | Get answer directly |
| Content format | Keyword-optimized pages | Structured, citable content |
| Success metric | Click-through rate | Citation frequency |
| Query type | Short keywords | Conversational, long-tail |
### The AI Search Landscape (2025-2026)
- **Google AI Overviews:** 2B+ monthly users across 200 countries ([TechCrunch](https://techcrunch.com/2025/07/23/googles-ai-overviews-have-2b-monthly-users-ai-mode-100m-in-the-us-and-india/))
- **Google AI Mode:** 100M+ monthly users in US and India
- **ChatGPT Search:** Real-time web search with citations
- **Perplexity AI:** Real-time citation engine, emphasis on freshness
- **Microsoft Copilot Search:** Bing integration with generative AI
- **Zero-click searches:** About 60% of global searches end without a click ([neotype.ai](https://neotype.ai/zeroclick-searches/))
## 2. Content Structure for AI Readability
### Semantic HTML Structure
AI systems extract information more effectively from well-structured content:
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Descriptive, Question-Answering Title</title>
</head>
<body>
<article>
<header>
<h1>Primary Topic as Question or Clear Statement</h1>
<p class="summary">Direct 2-3 sentence answer to the main question.</p>
</header>
<main>
<section>
<h2>Subtopic Heading</h2>
<p>Detailed explanation with facts and data.</p>
<ul>
<li>Key point 1 with specific information</li>
<li>Key point 2 with verifiable data</li>
<li>Key point 3 with actionable insight</li>
</ul>
</section>
</main>
<aside>
<h3>Quick Facts</h3>
<dl>
<dt>Term</dt>
<dd>Definition</dd>
</dl>
</aside>
</article>
</body>
</html>
```
### Heading Hierarchy Best Practices
```markdown
# H1: Main Topic (contains primary question/keyword)
└── ## H2: Major subtopic
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