name	aeo-optimization
description	AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

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

<!-- Article Schema -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "[Page Title]",
  "author": {
    "@type": "Person",
    "name": "[Author Name]",
    "url": "[Author Bio URL]"
  },
  "datePublished": "[ISO Date]",
  "dateModified": "[ISO Date]",
  "publisher": {
    "@type": "Organization",
    "name": "[Company]",
    "logo": "[Logo URL]"
  }
}
</script>

<!-- FAQ Schema (if FAQ section exists) -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "[Question 1]",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "[Answer 1]"
      }
    },
    {
      "@type": "Question",
      "name": "[Question 2]",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "[Answer 2]"
      }
    }
  ]
}
</script>

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)

aeo-optimization

Install Skill

SKILL.md