---
name: aeo-optimization
description: AI Engine Optimization (AEO) / Generative Engine Optimization (GEO) for maximizing AI discoverability across ChatGPT, Perplexity, Claude, Gemini, Copilot, and Google AI Overviews. Use this skill when (1) creating or optimizing landing pages, blog posts, or documentation for AI visibility, (2) implementing structured data or JSON-LD schemas, (3) building React components with AEO considerations, (4) developing content strategy for AI citation-worthiness, (5) auditing pages for AI discoverability, (6) answering questions about GEO, AEO, AI visibility, or how to get cited by AI systems.
---

# AI Engine Optimization (AEO/GEO)

Optimize frontend code and content to maximize visibility and citation-worthiness across AI platforms including ChatGPT, Perplexity, Claude, Gemini, Copilot, and Google AI Overviews.

## Core Principles: The AEO Triad

### 1. Structure (Machine-Readable Signals)
AI systems extract meaning from structured data. JSON-LD schemas provide explicit signals about content type, authorship, and relationships that AI can parse reliably.

### 2. Citation-Worthiness (Content Quality)
AI systems cite sources that provide authoritative, factual, well-organized information. Content must earn citation through clarity, accuracy, and usefulness.

### 3. Accessibility (Technical Discoverability)
Content must be crawlable and indexable. SPAs need special consideration. AI crawlers have different behaviors than traditional search bots.

## SEO vs AEO: Complementary Differences

| Aspect | Traditional SEO | AI Engine Optimization |
|--------|-----------------|------------------------|
| Primary goal | Rank in search results | Get cited in AI responses |
| Content format | Keyword-optimized | Answer-first, factual density |
| Signals | Backlinks, keywords | Structured data, E-E-A-T |
| User journey | Click through to site | Answer consumed in AI |
| Measurement | Rankings, clicks | Mentions, citations |
| Time horizon | Weeks to months | Immediate (live) + months (training) |

AEO complements SEO - well-structured, authoritative content benefits both.

## Quick Start Patterns

### Essential Structured Data

Every public page needs at minimum:
- **Organization schema** on homepage
- **WebSite schema** with search action
- **WebPage or Article schema** on content pages
- **FAQPage schema** where applicable

Basic implementation with useSEO hook:

```jsx
import { useSEO } from '@/hooks/useSEO';

function LandingPage() {
  useSEO({
    title: 'Product Name - Concise Value Proposition',
    description: 'Direct answer to what this product does. Specific benefits with measurable outcomes.',
    canonicalUrl: 'https://example.com/',
    schemas: [
      {
        id: 'organization-schema',
        data: {
          '@context': 'https://schema.org',
          '@type': 'Organization',
          name: 'Company Name',
          url: 'https://example.com',
          description: 'What the company does in one clear sentence.',
          // See references/structured-data-patterns.md for complete fields
        }
      },
      {
        id: 'website-schema',
        data: {
          '@context': 'https://schema.org',
          '@type': 'WebSite',
          name: 'Site Name',
          url: 'https://example.com',
          potentialAction: {
            '@type': 'SearchAction',
            target: 'https://example.com/search?q={search_term}',
            'query-input': 'required name=search_term'
          }
        }
      }
    ]
  });
  // ...
}
```

### Content Structure Pattern

Lead with direct answers, then elaborate:

```markdown
## What is [Topic]?

[Topic] is [one-sentence definition with key characteristics].

### Key Features

| Feature | Description | Benefit |
|---------|-------------|---------|
| Feature 1 | What it does | Why it matters |
| Feature 2 | What it does | Why it matters |

### How [Topic] Works

1. **First step**: Specific action with concrete details
2. **Second step**: Specific action with measurable outcome
3. **Third step**: Specific action with expected result

### Frequently Asked Questions

**Q: Common question about topic?**
A: Direct answer in 1-2 sentences. Additional context if needed.
```

### E-E-A-T Implementation

Experience, Expertise, Authoritativeness, Trustworthiness:

```jsx
// Author schema for blog posts
const authorSchema = {
  '@context': 'https://schema.org',
  '@type': 'Person',
  name: 'Author Name',
  url: 'https://example.com/about/author-name',
  jobTitle: 'Senior Engineer',
  worksFor: {
    '@type': 'Organization',
    name: 'Company Name'
  },
  sameAs: [
    'https://linkedin.com/in/author',
    'https://github.com/author'
  ]
};
```

## Implementation by Task

### Creating/Optimizing Pages
1. Read `references/structured-data-patterns.md` for appropriate schemas
2. Read `references/content-optimization.md` for content structure
3. Read `references/react-implementation.md` for technical patterns

### Auditing Existing Pages
1. Read `references/audit-checklist.md` for quick assessment
2. Test with Schema.org validator
3. Test AI retrieval across platforms

### Understanding AI Crawlers
1. Read `references/ai-crawler-guide.md` for crawler behaviors
2. Configure robots.txt appropriately
3. Consider prerendering for SPAs

### Content Strategy
1. Read `references/content-optimization.md` for citation patterns
2. Focus on factual density and answer-first structure
3. Implement multi-format redundancy

## Critical AEO Patterns

### Pattern 1: Factual Density

Bad:
> Our solution helps many companies improve their results.

Good:
> Our platform monitors brand mentions across 12 AI systems, tracking over 50,000 prompts daily with 94% accuracy in sentiment classification.

Specific numbers, measurable claims, and verifiable facts earn citations.

### Pattern 2: Answer-First Structure

Bad:
> When considering the various aspects of our approach, it's important to understand the historical context...

Good:
> Brand monitoring works by tracking AI responses to industry-relevant prompts. Here's how:
> 1. Define tracking prompts
> 2. Query AI platforms via API
> 3. Analyze responses for brand mentions

Lead with the answer. Elaborate after.

### Pattern 3: Q&A Format

Questions users actually ask, with direct answers:

```jsx
const faqSchema = {
  '@context': 'https://schema.org',
  '@type': 'FAQPage',
  mainEntity: [
    {
      '@type': 'Question',
      name: 'How do I track my brand in ChatGPT?',
      acceptedAnswer: {
        '@type': 'Answer',
        text: 'Track your brand in ChatGPT by querying the API with industry-relevant prompts and analyzing responses for brand mentions. Tools like visibility monitors automate this process.'
      }
    }
  ]
};
```

### Pattern 4: Hierarchical Authority

Structure content from general to specific:

```
# Category (H1)
## Subcategory (H2)
### Specific Topic (H3)
#### Implementation Detail (H4)
```

Use semantic HTML: `<article>`, `<section>`, `<nav>`, `<aside>`.

### Pattern 5: Multi-Format Redundancy

Present key information in multiple formats:

1. **Prose**: Narrative explanation
2. **Tables**: Comparison data
3. **Lists**: Step-by-step processes
4. **Code**: Implementation examples

AI systems may extract from any format - redundancy increases citation likelihood.

### Pattern 6: Source Attribution

Cite authoritative sources:
- Link to primary sources
- Reference industry standards
- Quote experts with attribution

Content that cites well is more likely to be cited.

## Common Mistakes

### Keyword Stuffing
AI systems detect unnatural keyword density. Write for humans; structured data handles machine signals.

### Thin Content
Pages with minimal substantive content won't earn citations. Each page should answer a specific question thoroughly.

### Missing Structured Data
Without JSON-LD, AI systems rely on inference. Explicit schemas dramatically improve extraction accuracy.

### SPA Rendering Issues
Client-rendered content may not be visible to crawlers. Implement prerendering or SSR for public-facing pages.

### Ignoring Live vs Training
AI systems have two access modes:
- **Live retrieval**: Real-time crawling (Perplexity, Copilot with search)
- **Training data**: Periodic crawl snapshots (base ChatGPT, Claude)

Optimize for both timeframes.

### Blocked Crawlers
Check robots.txt - some sites accidentally block AI crawlers while allowing Googlebot.

## Schema Quick Reference

### Organization
```json
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Company Name",
  "url": "https://example.com",
  "logo": "https://example.com/logo.png",
  "description": "What the company does.",
  "foundingDate": "2020",
  "address": {
    "@type": "PostalAddress",
    "addressLocality": "City",
    "addressRegion": "State",
    "addressCountry": "US"
  }
}
```

### Article
```json
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Article Title",
  "author": {
    "@type": "Person",
    "name": "Author Name"
  },
  "datePublished": "2024-01-15",
  "dateModified": "2024-01-20",
  "publisher": {
    "@type": "Organization",
    "name": "Publisher Name"
  }
}
```

### FAQPage
```json
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "Question text?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Answer text."
    }
  }]
}
```

See `references/structured-data-patterns.md` for complete schema library.

## Measurement

### Testing AI Retrieval

1. **ChatGPT**: Ask questions your content should answer
2. **Perplexity**: Search for your brand/topic, check citations
3. **Claude**: Query with relevant prompts
4. **Google AI Overview**: Search and check AI-generated summaries

### Key Metrics

- **Citation presence**: Is your content cited?
- **Citation accuracy**: Is the information correctly extracted?
- **Brand mention sentiment**: How is your brand characterized?
- **Competitive visibility**: How do you compare to competitors?

### Testing Structured Data

- Google Rich Results Test: https://search.google.com/test/rich-results
- Schema.org Validator: https://validator.schema.org/
- Browser DevTools: Check `application/ld+json` scripts

## Integration Notes

This skill integrates with existing codebase patterns:

- **useSEO hook**: Located at `src/hooks/useSEO.js` - supports multiple schemas
- **Organization schema**: Pattern at `src/seo/organizationSchema.js`
- **MDX blog system**: Content at `src/content/blog/`

When implementing, extend existing patterns rather than creating parallel systems.

## Reference Files

For detailed implementation guidance:

- `references/structured-data-patterns.md` - Complete JSON-LD schema library
- `references/content-optimization.md` - Content patterns for AI citation
- `references/react-implementation.md` - React/Vite technical patterns
- `references/ai-crawler-guide.md` - AI crawler behaviors and configuration
- `references/audit-checklist.md` - Quick-reference audit checklists
