Aeo

Answer Engine Optimization (AEO)

End-to-end practice of optimizing content to be cited by LLMs when they generate answers. Covers the technical foundations (how LLMs select sources), content structuring patterns (Q&A schema, citation-worthy patterns), measurement (which content gets cited, by which LLM, how often), and the strategic positioning that differentiates AEO from traditional SEO and from AI-SEO.

This skill is provider-aware but provider-agnostic: works for content optimized for ChatGPT, Claude, Perplexity, Gemini, Copilot, and emerging AI surfaces.

When to use this skill

| Situation | Skill applies |

|-----------|---------------|

| Designing content strategy that targets LLM citation | Yes — start with AEO fundamentals |

| Auditing existing content for LLM citability | Yes — scripts/aeo_content_auditor.py |

| Adding Q&A schema to content | Yes — scripts/schema_qa_generator.py |

| Tracking which content gets cited by LLMs | Yes — scripts/citation_extractor.py |

| Choosing between AEO and traditional SEO investment | Yes — see AEO vs SEO vs AI-SEO |

| Ranking in Perplexity / Google AI Overviews | Use marketing/ai-seo |

| Traditional SEO (rank in Google search results) | Use marketing/seo-specialist |

AEO vs SEO vs AI-SEO

Three distinct (but overlapping) practices. Confusing them leads to wasted investment.

| Practice | Optimizes for | Surface | Success metric |

|----------|---------------|---------|----------------|

| Traditional SEO | Google / Bing rankings | SERPs (organic blue links) | Position, clicks |

| AI-SEO | AI search engines | Perplexity, Google AI Overviews, You.com | Position in AI search results, traffic from citations |

| AEO (this skill) | LLM citation in answers | ChatGPT, Claude, Gemini, Copilot answers | Citation rate, brand mention in LLM outputs |

Strategic positioning

For most B2B brands:

• Traditional SEO: still 50-70% of organic traffic. Don't abandon.
• AI-SEO: emerging 10-20% of search-driven engagement. Growing fast.
• AEO: 5-15% of LLM-mediated user discovery. Largest growth potential.

Optimize content for all three simultaneously; the techniques substantially overlap.

The AEO funnel

Users find brands through LLMs in a different funnel than search:

Traditional search:           AEO funnel:
1. User types query           1. User asks LLM a question
2. SERPs show ~10 results     2. LLM generates answer
3. User clicks one            3. LLM cites N sources (1-10)
4. User reads page            4. User reads answer; may click cited source
5. User converts              5. User attributes answer to LLM (less so to cited brand)

Key implications:

• Citation is the new click. When LLM cites your content, you don't always get a visit — but you get attribution.
• Brand-as-source becomes the goal. Even without click, being cited builds brand association.
• Quality > volume. LLMs cite a small number of sources; quality of citation matters more than ranking position.
• Trust signals matter more. LLMs avoid citing low-authority sources.

See references/aeo-fundamentals.md for the deep mechanics of how LLMs select sources, the citation models per provider, and the trust signals that drive selection.

The 5 content patterns that get cited

After analysis of LLM citation behavior, five content patterns dominate:

Pattern 1: Definitional content with clear claims

LLMs cite sources for definitions, facts, and short claims. Pages that answer "What is X?" with a clean 2-3 sentence definition followed by elaboration get cited often.

Structure:

[Term] is [crisp definition in 1-2 sentences].

[Elaboration with context and nuance — 1-3 paragraphs].

[Related concepts / scope / boundaries — optional].

Pattern 2: Comparative tables

LLMs use tables to extract comparisons. Markdown tables in published content (or HTML equivalents) get cited when users ask "X vs Y."

| Feature | Product A | Product B |
|---------|-----------|-----------|
| Price | $X | $Y |
| Speed | Z ms | W ms |
| Support | 24/7 | Business hours |

Pattern 3: Step-by-step procedural content

"How to [task]" content with explicit numbered steps. LLMs reproduce procedural steps; the cited source becomes the authoritative reference.

Pattern 4: Statistics + data with sources

LLMs cite content that provides numerical facts with attribution. "According to [your study], X% of [thing] does Y" is repeatable and citable.

Pattern 5: Lists with explanations

"Top N approaches to X" with each item explained gets cited when users ask comparative or enumeration questions.

See references/llm-content-structuring.md for deep patterns including FAQ schema, citation hooks, voice-search optimization, and LLM-readable structure markers.

Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Target queries — the actual questions customers ask LLMs about your category (drives which content to audit and restructure)
• [ ] Your brand name — exact wording to track in answers vs competitors (drives citation extraction)
• [ ] Target LLM surface — ChatGPT / Claude / Perplexity / Gemini (citation behavior and trust signals differ per provider)
• [ ] Canonical page/content — the high-value page to be the authoritative source (drives schema generation + pattern restructuring)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick start

• Audit existing content: python3 scripts/aeo_content_auditor.py --path ./content
• Add Q&A schema to high-value pages: python3 scripts/schema_qa_generator.py --content article.md
• Track citations from competitors: python3 scripts/citation_extractor.py --query "What is X?" --brand "Your Brand"
• Iterate: monthly content review with AEO scoring

End-to-end workflows

Workflow: AEO content strategy from scratch

• Identify target queries — what questions do potential customers ask LLMs about your category?
• Audit competitor citations — which brands get cited for those queries? scripts/citation_extractor.py
• Audit your existing content — score current content for AEO patterns: scripts/aeo_content_auditor.py
• Prioritize 10-20 high-value pages — those that should be the canonical source
• Restructure per AEO patterns — definitional content, tables, step-by-step, statistics
• Add structured data — scripts/schema_qa_generator.py generates FAQ schema
• Build authority signals — backlinks, citations, mentions
• Monitor monthly — track citation rate trend

Workflow: Audit individual content piece

• Run scripts/aeo_content_auditor.py --path article.md --format markdown
• Review per-pattern scoring (5 patterns above)
• Identify gaps: missing definition, no table, no clear steps, no stats, no list
• Restructure to add 2-3 missing patterns
• Add FAQ schema with scripts/schema_qa_generator.py
• Re-audit to confirm improvements

Workflow: Competitive citation analysis

• Identify 10-20 key queries in your category
• Query each LLM (ChatGPT, Claude, Perplexity, Gemini) with those questions
• Record citations + brands mentioned
• Analyze: which brands dominate? what content do they have?
• Identify white-space queries (no clear dominant source yet)
• Prioritize content creation for white-space queries

Workflow: Measure AEO performance

• Citation rate: % of queries where your brand is cited (target: 30%+ for category leaders)
• Brand mention rate: % of queries where your brand is mentioned (cited or not)
• Source quality: are you cited as primary source or supporting?
• Click-through from citations: traffic attributable to LLM citations (requires source tracking)
• Voice tracking: how is your brand characterized (positive / neutral / negative attributes)

See references/citation-tracking-and-measurement.md for measurement methodologies, attribution challenges, and competitive benchmarking.

Common AEO failures

• Optimizing only for Google SERP: misses the LLM citation surface entirely
• Generic content without specific claims: LLMs prefer specific, factual content over generic explanation
• No structure markers (headings, lists, tables): LLMs can't extract specific information
• No FAQ schema: missed opportunity for Q&A surfacing in AI Overviews
• Stuffed keyword content: LLMs prefer natural language with clear meaning
• No authority signals: LLMs avoid citing low-trust sources
• Outdated content: LLMs prefer recent, current content
• Hidden behind paywalls: LLMs can't cite what they can't access
• No structured data: missed opportunity for richer extraction
• Brand-first content: LLMs prefer informational content over promotional

LLM-by-LLM citation behavior

Different LLMs have different citation behaviors:

| LLM | Citation style | What gets cited |

|-----|----------------|-----------------|

| ChatGPT | Inline citations (when web-enabled); fewer otherwise | Recent, authoritative sources |

| Claude | Citations when grounding enabled (tools); generally avoids unsupported claims | High-quality sources, evidence-based |

| Perplexity | Always cites sources prominently | Recent + authoritative sources |

| Google Gemini / AI Overviews | Cites in AI Overviews + Gemini responses | High-ranking pages + structured data |

| Copilot (Microsoft) | Cites sources prominently | Sources varied |

| Meta AI | Lighter citation | Limited transparency |

Optimize content with structure markers (headings, lists, tables) and authority signals (links, citations, expert attribution) — works across all of these.

Tooling

| Script | Purpose |

|--------|---------|

| scripts/aeo_content_auditor.py | Score content for AEO patterns (definition, table, steps, stats, list, structure markers) |

| scripts/citation_extractor.py | Parse LLM responses (saved transcripts) for brand citations + competitive analysis |

| scripts/schema_qa_generator.py | Generate JSON-LD FAQ schema from content (FAQPage / QAPage / HowTo) |

References

• aeo-fundamentals.md — how LLMs select sources; citation mechanisms per provider; trust signals
• llm-content-structuring.md — content patterns; Q&A schema; voice-search; structure markers
• citation-tracking-and-measurement.md — measurement methodologies; attribution; benchmarking

Related skills

• marketing/ai-seo — AI search engine ranking (Perplexity, Google AI Overviews); complementary to AEO
• marketing/seo-specialist — traditional SEO (Google rankings); foundational; still 50-70% of organic
• marketing/seo-audit — technical SEO audit
• marketing/programmatic-seo — scaled content production with SEO patterns
• c-level-advisor/cs-cmo-advisor — strategic AEO investment decisions