AI Engine Optimization (AEO) Skill

Load with: base.md + web-content.md + site-architecture.md

Purpose: Optimize content for AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) so your brand gets cited in AI-generated answers.

Source: Based on HubSpot's AEO Guide and industry best practices.

Why AEO Matters Now ┌────────────────────────────────────────────────────────────────┐ │ THE GREAT DECOUPLING │ │ ──────────────────────────────────────────────────────────── │ │ Impressions ≠ Clicks anymore. │ │ AI engines compile answers from multiple sources. │ │ More buyer journey happens inside chat experiences. │ │ 58% of Google searches = zero clicks (AI overviews). │ ├────────────────────────────────────────────────────────────────┤ │ THE OPPORTUNITY │ │ ──────────────────────────────────────────────────────────── │ │ Shape what AI engines say about your category and product. │ │ Get cited as the authoritative source. │ │ Best answer > Best page ranking. │ └────────────────────────────────────────────────────────────────┘

Key Stats:

70% of consumers use ChatGPT for searches 47% of Google queries show AI overviews Average ChatGPT prompt: 23 words (vs 4.2 for Google) AEO market: $886M (2024) → $7.3B (2031) How AI Engines Choose Answers

AI engines use three main signals to select content for answers:

1. Consensus

Facts that appear across multiple credible sources get trusted and reused.

How to build consensus:

Repeat key facts consistently across your own pages Use same terminology as industry leaders Link to and from authoritative external sources Create internal content clusters that reinforce each other 2. Information Gain

Net-new insight beats generic advice. AI engines prefer content that adds value.

How to add information gain:

Original research and data Concrete examples with specifics Clear point of view (not fence-sitting) Expert quotes with credentials Case studies with metrics 3. Entities & Structure

Clear entities and tidy structure reduce ambiguity and boost quotability.

How to optimize structure:

Use semantic triples (Subject → Verb → Object) Clear headings with entity names Schema markup (Article, FAQ, Product) Short, scannable paragraphs (2-4 sentences) Semantic Triples (Critical for AEO)

What they are: Compact facts that AI engines (and humans) can't misread.

Pattern: [Subject] [verb] [object].

Examples ✅ GOOD (clear triples): - HubSpot CRM syncs contact and company data. - Lead Scoring assigns priority based on engagement. - Workflows trigger email sequences from events.

❌ BAD (vague, no clear entity): - The system helps with various tasks. - It can do many things for users. - This improves overall performance.

Triple Checklist

For every key claim, ask:

Is the subject a clear entity (product, feature, brand)? Is the verb specific and active? Is the object concrete and measurable? Paragraph Pattern (Feature → How → Outcome)

Every substantive paragraph should follow this structure:

[Feature] helps [User/Role] with [Job]. It [mechanism/inputs] to [process]. Teams see [metric/result] in [timeframe/context].

Triples: - [Subject] [verb] [object]. - [Subject] [verb] [object].

Example Lead Scoring helps sales teams prioritize prospects. It combines page views, email engagement, and firmographic data to assign a numeric score, then auto-enrolls high scorers into follow-up sequences. Reps focus on qualified accounts and book 40% more meetings.

• Lead Scoring assigns scores from engagement data.
• High scorers trigger automated follow-up sequences.

Page Templates Template 1: Category Explainer

Goal: Define the category, tie it to your product, earn citations.

What is [Category]? — [1-2 line value promise]

What is [Category]? (~80 words)

[Plain definition in everyday language. Name adjacent entities.]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

Why it matters now (~60 words)

[One paragraph. Mention shift to answers over links; tie to buyer outcomes.]

How to apply it (3-5 bullets)

• [Action 1]
• [Action 2]
• [Action 3]

FAQ

Q: [Question]? A: [~1 sentence answer]

Q: [Question]? A: [~1 sentence answer]

Q: [Question]? A: [~1 sentence answer]

Links: [Category hub] | [Product/Feature] | [Credible source 1] | [Credible source 2] CTA: [Demo / Template / Signup] Schema: Article + FAQ. Author + last updated.

Template 2: Product & Feature Page

Goal: Clarify capability, fit, and next step; reinforce category linkage.

[Product/Feature] — [Outcome in 3-5 words]

[Product/Feature] enables [Outcome] for [User/Role].

[Feature Area 1]

[2-4 sentences using Feature → How → Outcome]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

[Feature Area 2]

[2-4 sentences using Feature → How → Outcome]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

[Feature Area 3]

[2-4 sentences using Feature → How → Outcome]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

FAQ

Q: [Question]? A: [~1 sentence]

Q: [Question]? A: [~1 sentence]

Q: [Question]? A: [~1 sentence]

Links: Back to [Category Explainer] | Forward to [Demo/Trial] Proof: [Benchmark/Analyst/Customer proof] Notes: Requirements/limits (pricing tier, integrations) Schema: Article + FAQ. Author + last updated.

Template 3: Comparison / Alternatives Page

Goal: Help readers decide with clear criteria; earn fair citations.

[Product] vs. [Alternative] — Which fits [Use case]?

Comparison Table

| Criterion | [Product] | [Alt A] | [Alt B] | Source |

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

| [Feature/Limit] | [value] | [value] | [value] | [link] |

| [Requirement] | [value] | [value] | [value] | [link] |

| [Best for] | [value] | [value] | [value] | [link] |

Source-back all claims in the table or footnotes.

Fit Statements

1. [Product] suits [Team/Use case] when [Condition].
2. [Alt A] fits [Team/Use case] when [Condition].
3. [Alt B] works for [Team/Use case] when [Condition].

Links: [Category Explainer] | [Feature pages] CTA: [Try / Demo / Talk to Sales] Schema: Article. Author + last updated.

Template 4: Use Case / Industry Page

Goal: Connect product to outcomes in a context readers recognize.

[Industry/Use Case] — [Outcome KPI]

Teams reduce [Metric] by [Y%] in [Timeframe].

Mini Case Study

[Company/Role] used [Product/Feature] to [Action], resulting in [Metric improvement] within [Timeframe].

How It Works

[Feature 1]

[Feature → How → Outcome paragraph]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

[Feature 2]

[Feature → How → Outcome paragraph]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

Who Uses This

Roles: [Role 1], [Role 2], [Role 3] Workflows: [Workflow 1], [Workflow 2] Integrations: [Integration 1], [Integration 2]

Links: [Product/Feature pages] | [Supporting blog] CTA: [Industry template / Demo variant] Schema: Article. Author + last updated.

Template 5: Supporting Blog Post

Goal: Add information gain and support your content cluster.

[Topic] — [Specific promise]

Opening (~60-80 words)

[State the problem. Align terminology with Category Explainer. Preview outcome.]

[Section 1 Heading] (~120 words max)

[Feature → How → Outcome]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

Internal link: [Related page] External citation: [Credible source]

[Section 2 Heading] (~120 words max)

[Feature → How → Outcome]

Triples: 1. [Subject] [verb] [object]. 2. [Subject] [verb] [object].

Internal link: [Related page] External citation: [Credible source]

Key Takeaway

[1-2 lines summarizing the main point]

CTA: [Single primary action]

Schema: Article. Author + last updated.

Site-Wide Trust Signals Required on Every Page Element Implementation Schema markup Article + FAQ (if FAQ exists) Author attribution Name, bio, credentials, photo Last updated date Visible, machine-readable Internal links 3-5 per page (upstream/downstream) External citations 1-2 credible sources per section Single CTA Demo, template, or signup (repeated once near end) Schema Implementation

Content Cluster Architecture ┌─────────────────────┐ │ Category Explainer │ │ "What is AEO?" │ └──────────┬──────────┘ │ ┌──────────────────────┼──────────────────────┐ │ │ │ ▼ ▼ ▼ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ Product Page │ │ Product Page │ │ Product Page │ │ "Feature A" │ │ "Feature B" │ │ "Feature C" │ └───────┬───────┘ └───────┬───────┘ └───────┬───────┘ │ │ │ ▼ ▼ ▼ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ Blog Post │ │ Use Case │ │ Comparison │ │ (supports) │ │ (industry) │ │ (vs. alt) │ └───────────────┘ └───────────────┘ └───────────────┘

Linking Rules:

Category Explainer links DOWN to all product pages Product pages link UP to Category Explainer Product pages link ACROSS to related features Blog posts link UP to Product pages Comparison pages link to Category Explainer + relevant Product pages AEO Writing Checklist Per-Paragraph Checklist Follows Feature → How → Outcome pattern Contains 2-4 sentences (scannable) Includes 1-2 semantic triples Names specific entities (not vague "it" or "this") Uses active voice verbs Per-Section Checklist Has 1 internal link (upstream or downstream) Has 1 external citation (credible source) Section heading names an entity ~120 words max Per-Page Checklist H1 contains primary entity + value promise Opening claim is a semantic triple 3-5 internal links total 1-2 external citations total Mini-FAQ with 3 questions (if applicable) Single primary CTA Schema markup (Article + FAQ) Author name + bio link Last updated date visible Site-Wide Checklist Category Explainer exists for each key category Product pages link back to Category Explainer Content cluster architecture documented Author bio pages exist with credentials Consistent terminology across all pages Measuring AEO Success Key Metrics Metric How to Track AI citations Manual checks in ChatGPT, Claude, Perplexity Brand mentions in AI Search "[brand] + [category]" in AI engines Share of answer How often you're cited vs competitors LLM traffic GA4 referral from chatgpt.com, claude.ai, perplexity.ai Impressions-to-clicks gap GSC impressions vs actual clicks Tools HubSpot AEO Grader - Grade your brand's AI visibility Google Analytics 4 - Track LLM referral traffic Google Search Console - Monitor impressions vs clicks gap Manual AI queries - Regularly test your brand in AI engines Common AEO Mistakes Mistake Fix Vague language ("it helps with things") Use specific entities and triples No clear structure Use Feature → How → Outcome Missing schema Add Article + FAQ schema No author attribution Add author name, bio, credentials Generic content Add original data, examples, POV Orphan pages Link into content cluster Fence-sitting ("it depends") Take a clear position No external citations Add 1-2 credible sources per section AEO vs Traditional SEO Aspect Traditional SEO AEO Goal Rank on page 1 Get cited in AI answers Success metric Click-through rate Share of answer Content focus Keywords Entities + facts Structure Headers for scanning Triples for extraction Links Backlinks for authority Citations for consensus Updates Periodic refresh Continuous accuracy Quick Reference Semantic Triple Pattern [Entity/Product] [active verb] [concrete object/result].

Paragraph Pattern [Feature] helps [User] with [Job]. It [mechanism] to [process]. Teams see [result] in [timeframe].

Page Minimums 3-5 internal links 1-2 external citations per section 3 FAQ questions with schema Author + last updated Single CTA Content Hierarchy Category Explainer (top) Product/Feature pages (middle) Use case / Comparison / Blog (supporting)