Build An AEO Content Strategy To Improve Citation Opportunities

AEO Guide: Content Strategy

Use Query Fan-Out to Adapt Your Content Strategy for AI Search

Learn how query fan-out shapes which content AI systems reuse and how adapting your strategy to that behavior can improve your visibility in AI search.

Creating Content for Answer Engine Optimization

Creating content for that’s optimized for answer engines means writing for three audiences simultaneously:

The humans who need answers,
The AI systems that retrieve and reuse your content, and
The search engines that continue to crawl, index, and rank your pages.

While on-page practices may make your content easier for generative engines to retrieve, considering how your content fits together as a whole can increase the likelihood that your brand will become a recognized authority for information within your subject matter expertise.

Building Topical Clusters for Query Fan-Out

AI systems don’t evaluate content in isolation. They recognize topical authority through a comprehensive cluster of related content. Planning your content in linked page clusters will be more effective than tweaking the formatting on individual pages alone.

Topical clusters demonstrate your expertise across related concepts while creating multiple citation opportunities. This approach aligns with how retrieval-augmented generation (RAG) systems process queries through query fan-out.

How Query Fan-Out Triggers Citation Opportunities

AI search systems do more than answer the question you ask. They anticipate what you’ll ask next and expand single queries into multiple related questions or topics. Then they seek out content to provide an answer for each of those related terms.

This behavior, called query fan-out, is central to how AI systems gather and synthesize information.

AI systems retrieve content chunks that address each expanded question, then weave them into comprehensive responses. Sometimes this information comes from one source, but often it’s aggregated from several.

This creates multiple citation opportunities for content that addresses the full question progression rather than just an initial query.

Topic clusters naturally address this expanded retrieval pattern by covering the full spectrum of questions users ask about a subject, making your content more likely to be selected across multiple parts of AI-generated responses. Planning your content in linked page clusters will be more effective than tweaking the formatting on individual pages alone.

How To Plan a Content Cluster for Query Fan-Out

To capitalize on these citation opportunities in a way that supports revenue generation, we recommend organizing your content in a cluster with a central commercial page and related informational pages.

The pillar page carries commercial intent and supports bottom-of-the-funnel conversion, while informational pages address the related questions prospective customers ask as they recognize a need and evaluate solutions. This architecture supports coverage across the fan-out and creates clear content relationships that AI systems can follow. It also aligns content to the customer journey and helps build brand trust over time.

What To Include in a Query Fan-Out–Driven Cluster

Build your clusters around three key components:

1. Pillar page: Provides topic overview with sections that can be cited independently.

2. Supporting pages: Deep-dive content that answers specific questions in detail.

3. Strategic linking: Connect topically related pages by linking supporting pages to pillar pages with descriptive anchor text and vice versa.

Depending on the queries related to your subject matter, you might want to build additional clusters centered around one or two of your supporting pages for more complex topical coverage.

Once you’ve mapped your clusters, the next step is formatting individual pages so AI systems can easily extract and cite your content. The goal is to make content modular without sacrificing readability or search engine optimization.

How To Format Content for Retrieval

Not every piece of content needs this level of optimized formatting. Focus on content types that AI search engines are most likely to cite: definitions, explanations, how-to guides, comparisons, and FAQ-style content.

Create clear heading hierarchies.

Use H2 and H3 tags to establish a clear content structure:
- H2 for major topics, framed as questions when appropriate (“How Do You Implement Schema Markup?”).
- H3 for related subtopics or implementation steps.
Try to use natural language that reflects how people actually search. Your headings should form a clear outline that makes sense on its own.
Structure content in extractable chunks.

Break information into sections to enhance readability and scannability:
- Lead with direct answers: Each section should begin with a clear, complete response to the stated or implied question in the heading.
- Keep paragraphs short: For most copy, stick to 2-3 sentences maximum to improve both readability and extraction opportunities.
- Write self-contained sections: Each chunk should make sense on its own without context from other sections.
- Avoid pronoun dependency: Repeat key subjects instead of using pronouns without a clear reference.
Use visual elements that aid extraction.

Integrate formatting elements that make information easy to scan and extract:
- Bullet points for key characteristics or benefits: Share product features, concept definitions, or summary points in a list.
- Numbered lists for sequential processes: Give users step-by-step instructions or ranked recommendations.
- FAQ sections for addressing common follow-up questions: Anticipate what users ask next about your topic and provide easy-to-retrieve answers.
- Tables for comparisons and structured data: Provide side-by-side feature comparisons, pricing information, or process steps.
Link strategically.

While internal links support topical authority and help AI systems understand content relationships, our research shows that where you place those links may influence citation likelihood.

75% of the content cited in AI overviews contains no internal links. We recommend you avoid links in answer-focused paragraphs. Use descriptive anchor text to clearly describe what the linked content covers.

Link between related pages to highlight how pieces connect within broader topics.

These formatting techniques are most effective as an integrated part of your content production process. Rather than focusing on one-off optimizations, integrate retrievability formatting into your content from the start.

Integrating AEO Into Your Content Process

While updating your existing content library is valuable, integrating these practices into your content creation process will help you build retrievability into every piece you publish.

Ideation and planning: Plan your content in topical clusters for comprehensive coverage.
Briefing: Update content briefs to include retrievability requirements like question-based headings, summary-first sections, FAQ sections, and tables for comparisons.
Writing: Update your style guide to reflect AEO formatting principles.
Editing and QA: Verify that AEO writing and formatting principles are in place.
Content audits: When reviewing published content, use tools to note which pieces get cited and which formatting elements seem to drive citations.

AEO as an Extension of SEO

As AI systems increasingly shape how people research options, compare solutions, and make a decision, visibility depends as much on whether systems can confidently reuse what you publish as it does on where a page ranks in traditional search.

While answer engine optimization might represent an evolution in how we create and structure content, it doesn’t replace existing SEO best practices.

The principles covered here, including building topical clusters that map to related queries, formatting pages for extraction, and integrating retrievability into your process, will increase your brand’s discoverability because they make your content clearer and more useful to both humans and machines.