Files
2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/AI-Answer-Engine-Optimization.md
T
Antigravity Agent c24165b8bc refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-11 11:05:56 +09:00

5.2 KiB

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, tech_stack
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 tech_stack
wiki-2026-0508-ai-answer-engine-optimization AI Answer Engine Optimization (AEO) 10_Wiki/Topics verified self
AEO
GEO
generative engine optimization
AI search SEO
citation optimization
none B 0.85 conceptual
aeo
geo
seo
llm-search
structured-data
ssr
json-ld
content-strategy
2026-05-09 pending Claude Opus 4.7 (manual cleanup 2026-05-09)
language framework
HTML / JSON-LD Next.js / Astro / SSR

AI Answer Engine Optimization (AEO)

📌 한 줄 통찰

"Search 의 click → AI answer 의 citation". ChatGPT / Perplexity / Google AI Overviews 의 매 brand 의 source 의 selection. SSR + JSON-LD + semantic HTML + Q&A format.

📖 핵심

매 search engine 의 evolution

  • 옛 SEO: keyword + ranking → click.
  • AEO: AI 의 answer 의 citation source.
  • GEO (Generative Engine Optimization): same idea.

매 AI bot 의 behavior

  • GPTBot, Claude-Web, PerplexityBot: crawl + summarize.
  • JS execution X: cost. 매 SSR / SSG 의 essential.
  • Cite recent sources: freshness.
  • Trust signal: domain authority, structured data.

Core technique

1. SSR / SSG (JS Execution Wall 제거)

  • 매 SPA 의 JS-only render = bot 의 invisible.
  • 매 SSR (Next, Astro) = HTML 의 first paint.

2. Semantic HTML

<article>
  <h1>Topic</h1>
  <main>
    <p>Direct answer in first paragraph.</p>
    <h2>Question 1?</h2>
    <p>Answer.</p>
  </main>
</article>

3. JSON-LD structured data

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "...",
  "author": { "@type": "Person", "name": "..." },
  "datePublished": "2026-05-09",
  "mainEntity": {
    "@type": "FAQPage",
    "mainEntity": [{
      "@type": "Question",
      "name": "How to X?",
      "acceptedAnswer": { "@type": "Answer", "text": "..." }
    }]
  }
}
</script>

4. Q&A format

  • H2 = question.
  • 매 H2 직후 = direct answer.
  • 매 paragraph 의 self-contained.

5. Direct answer in lead

  • 매 first 50 word 의 answer.
  • 매 inverted pyramid (journalism).

6. Citation-friendly

  • 매 specific number / fact.
  • 매 source 의 explicit.
  • 매 author 의 expertise.

7. llm.txt / robots.txt control

# llm.txt (proposal)
# Allow ChatGPT, Claude, Perplexity to cite.

User-agent: *
Allow: /

매 platform 의 difference

Google AI Overviews (SGE)

  • 매 YMYL (Your Money Your Life) 의 strict.
  • 매 E-E-A-T (Experience, Expertise, Authority, Trust).
  • 매 traditional SEO 의 baseline.

ChatGPT / Claude / Perplexity

  • Real-time web search.
  • 매 cite source.
  • Domain trust signal.

Bing Chat / Copilot

  • Edge integration.
  • 매 enterprise 친화.

💻 Code

Next.js SSR

// app/article/[slug]/page.tsx
export default async function ArticlePage({ params }) {
  const article = await fetchArticle(params.slug);
  
  return (
    <>
      <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify({
        '@context': 'https://schema.org',
        '@type': 'Article',
        headline: article.title,
        author: { '@type': 'Person', name: article.author },
        datePublished: article.publishedAt,
      })}} />
      
      <article>
        <h1>{article.title}</h1>
        <p>{article.lead}</p>
        <main>{article.content}</main>
      </article>
    </>
  );
}

FAQ 의 schema

function FAQ({ items }: { items: { q: string; a: string }[] }) {
  return (
    <>
      <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify({
        '@context': 'https://schema.org',
        '@type': 'FAQPage',
        mainEntity: items.map(({ q, a }) => ({
          '@type': 'Question',
          name: q,
          acceptedAnswer: { '@type': 'Answer', text: a },
        })),
      })}} />
      
      <section>
        {items.map(({ q, a }) => (
          <>
            <h2>{q}</h2>
            <p>{a}</p>
          </>
        ))}
      </section>
    </>
  );
}

🤔 결정 기준

작업 추천
Blog / docs SSG + JSON-LD
매 product page SSR + Product schema
FAQ FAQPage schema
매 SPA SSR fallback 추가
매 SaaS E-E-A-T content

기본값: SSR/SSG + JSON-LD + Q&A format + direct answer in lead.

🔗 Graph

🤖 LLM 활용

언제: 매 content site 의 AI traffic 의 capture. 매 documentation site 의 visibility. 언제 X: 매 internal app. 매 content gating (paywalled).

안티패턴

  • SPA + no SSR: bot 의 invisible.
  • JSON-LD 의 fake: penalty.
  • Click-bait title + AI 의 misalign: low citation rate.
  • 모든 page 의 same FAQ: spam.

🧪 검증 / 중복

🕓 Changelog

날짜 변경 처리 신뢰도
2026-05-08 Phase 1 정규화 UPDATE A
2026-05-09 Manual cleanup — code + technique + 결정 + 안티패턴 UPDATE B