2nd/10_Wiki/Topics/AI_and_ML/AI-Search-Optimization.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, inferred_by

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

<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)

<!-- 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)

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)

# 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

🤖 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