---
id: wiki-2026-0508-ai-search-optimization
title: AI Search Optimization
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI SEO, GEO, generative engine optimization, semantic SEO, entity SEO]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [ai-search, geo, aeo, semantic-seo, knowledge-graph, entity-seo, e-e-a-t, future-of-search]
raw_sources: []
last_reinforced: 2026-05-09
github_commit: pending
inferred_by: Claude Opus 4.7 (manual cleanup 2026-05-09)
---

# AI Search Optimization

## 📌 한 줄 통찰
> **"Keyword density → Entity authority"**. 매 LLM 의 knowledge graph 의 inclusion. **Semantic + structured + machine-readable + E-E-A-T**. AEO + GEO 의 umbrella.

## 📖 핵심

### 매 search engine 의 era
1. **Keyword era** (1995-2010): density.
2. **Link era** (2000-2015): PageRank.
3. **Semantic era** (2015-2023): BERT, RankBrain.
4. **Generative era** (2023+): SGE, ChatGPT, Claude, Perplexity.

### AI Search 의 매 component

#### 1. Crawling
- 매 bot (GPTBot, ClaudeBot, PerplexityBot, GoogleBot).
- 매 robots.txt 의 control.
- 매 freshness signal.

#### 2. Indexing
- 매 content 의 vector embedding.
- 매 entity recognition.
- 매 relationship extract.

#### 3. Retrieval
- 매 query 의 vector match.
- 매 hybrid (keyword + semantic).
- 매 freshness boost.

#### 4. Generation
- 매 LLM 의 매 retrieved context 의 synthesize.
- 매 citation.

### 매 optimization category

#### GEO (Generative Engine Optimization)
- 매 generative model 의 cite 의 source.
- 매 SSR + structured data + Q&A.

#### AEO (Answer Engine Optimization)
- 매 specific question 의 direct answer.
- 매 FAQ schema + featured snippet.

#### Semantic SEO
- 매 entity + relationship.
- 매 topic cluster.
- 매 internal link 의 graph.

#### LLM-friendly content
- 매 short paragraph.
- 매 numbered / bulleted.
- 매 self-contained.

### 매 strategy

#### Topic cluster
- 매 hub page (broad topic).
- 매 spoke page (specific subtopic).
- 매 internal link 의 hub-spoke.

#### Entity-first
- 매 specific entity (Person, Place, Product).
- 매 schema.org 의 explicit.
- 매 Wikipedia / Wikidata 의 link.

#### E-E-A-T (Google)
- Experience: first-hand.
- Expertise: credential.
- Authoritativeness: domain.
- Trustworthiness: secure.

#### Freshness
- 매 update date.
- 매 recent example.
- 매 stale content 의 refresh / archive.

### 매 platform 의 optimization

#### Google
- Core Web Vitals.
- E-E-A-T.
- AI Overviews / SGE.

#### ChatGPT (Browse / Plugins)
- 매 well-structured page.
- 매 citation-friendly.

#### Perplexity
- 매 freshness.
- 매 academic / authoritative.

#### Claude
- 매 long-form context.
- 매 detailed reasoning.

#### Bing / Copilot
- Bing webmaster.
- IndexNow protocol.

### 매 metric

#### Traditional
- Organic traffic.
- Keyword ranking.
- Backlink profile.

#### AI-era
- AI Overview inclusion rate.
- LLM citation rate (manual check).
- Brand mention 의 LLM context.
- Knowledge graph presence.

→ 매 measure 의 emerging tool.

## 💻 Code

### Schema.org Person + Article (E-E-A-T)
```html
<script type="application/ld+json">
[
  {
    "@context": "https://schema.org",
    "@type": "Article",
    "headline": "Topic",
    "datePublished": "2026-05-09",
    "dateModified": "2026-05-09",
    "author": {
      "@type": "Person",
      "name": "Jane Doe",
      "jobTitle": "Senior AI Researcher",
      "url": "https://janedoe.com"
    },
    "publisher": {
      "@type": "Organization",
      "name": "Acme",
      "logo": { "@type": "ImageObject", "url": "..." }
    }
  },
  {
    "@context": "https://schema.org",
    "@type": "Person",
    "@id": "https://janedoe.com#person",
    "name": "Jane Doe",
    "alumniOf": { "@type": "Organization", "name": "MIT" },
    "knowsAbout": ["Machine Learning", "AI Ethics"]
  }
]
</script>
```

### Topic cluster (internal link)
```html
<!-- Hub: /ai-search -->
<h1>AI Search Guide</h1>
<ul>
  <li><a href="/ai-search/aeo">AEO</a></li>
  <li><a href="/ai-search/geo">GEO</a></li>
  <li><a href="/ai-search/sge">SGE</a></li>
</ul>

<!-- Spoke: /ai-search/aeo links back to hub -->
<a href="/ai-search">Back to AI Search Guide</a>
```

### IndexNow (Bing / Yandex)
```bash
curl -X POST https://api.indexnow.org/indexnow \
  -H 'Content-Type: application/json' \
  -d '{
    "host": "example.com",
    "key": "...",
    "urlList": ["https://example.com/page1", "https://example.com/page2"]
  }'
```

### llms.txt (proposal, 2024)
```txt
# llms.txt
# Allow LLM crawler 의 cite

User-agent: *
Allow: /

# Specific high-value content
Sitemap: https://example.com/llm-sitemap.xml
```

## 🤔 결정 기준

| Site type | Priority |
|---|---|
| Blog / docs | GEO + AEO + topic cluster |
| E-commerce | Product schema + review |
| News | Freshness + Article schema |
| Local | LocalBusiness + GMB |
| YMYL | E-E-A-T strict |
| SaaS | Use case + comparison content |

**기본값**: SSR + schema.org + topic cluster + E-E-A-T author + Core Web Vitals.

## 🔗 Graph
- 부모: [[SEO]]
- 변형: [[AI-Answer-Engine-Optimization]] · [[AI-Overviews-and-SGE]] · [[Generative-Engine-Optimization]]
- 응용: [[Entity-SEO]]
- Adjacent: [[Knowledge Graph|Knowledge-Graph]]

## 🤖 LLM 활용
**언제**: Public content site 의 AI traffic 의 strategy.
**언제 X**: Internal app. Paid-gated content.

## ❌ 안티패턴
- **Keyword stuffing**: legacy 의 dead.
- **Schema spam**: penalty.
- **No internal link**: 매 page 의 isolated.
- **AI-generated mass content**: low quality flag.

## 🧪 검증 / 중복
- Verified (concept).
- 신뢰도 B.
- Overlap with [[AI-Answer-Engine-Optimization]] / [[AI-Overviews-and-SGE]] (different focus).

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-09 | Manual cleanup — strategy + code + metric + 결정 |
