What content formats actually get cited by AI? Testing different approaches

Your observations match the research. Here’s what the data shows:

Content format citation rates:

Why comparisons dominate: AI systems are often asked “What’s the best X?” or “X vs Y” questions. Comparison content directly answers these.

The FAQ effect: FAQs mirror natural language queries. When someone asks “How do I…” the Q&A format provides exact match content.

Table advantage: Tables showed 47% higher citation rates because:

• Easy to parse programmatically
• Contains structured, comparable data
• Perfect for extraction into AI responses

FAQ pages changed everything for us. Here’s our data:

Before FAQ restructuring:

• 12 pages ranking top 10 on Google
• 0 AI citations tracked
• No FAQ schema

After FAQ restructuring:

• Same 12 pages, converted to FAQ format
• 8 pages now cited by AI
• Full FAQPage schema implemented

What we changed:

1. Added FAQ section to every page
2. Converted H2s to question format
3. Placed direct answers immediately after questions
4. Implemented FAQPage schema
5. Added internal links between related FAQs

Time to results:

• Perplexity: Cited within 2 weeks
• Google AI Overview: 6 weeks
• ChatGPT (browsing): 4 weeks

The key insight: We didn’t create new content. We restructured existing content. Same information, better format = AI visibility.

Tables are underrated for AI. Here’s how to optimize them:

Good table structure:

<table>
  <thead>
    <tr>
      <th>Feature</th>
      <th>Tool A</th>
      <th>Tool B</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Price</td>
      <td>$10/mo</td>
      <td>$15/mo</td>
    </tr>
  </tbody>
</table>

Why proper HTML matters: AI systems parse HTML structure. Semantic markup helps them understand relationships.

Table best practices:

• Clear, descriptive headers
• Consistent data types in columns
• No merged cells
• Include table caption/summary
• Keep to 3-5 columns max

What to avoid:

• Tables made from divs/CSS
• Complex nested tables
• Tables for layout (not data)
• Missing header row

Our test results: Properly structured HTML tables: 47% higher citation rate CSS-styled div “tables”: 12% citation rate

Use real tables.

Listicles work great but structure matters:

High-performing listicle structure:

H1: Best [Category] in 2025

Quick summary: Top 3 picks with 1-sentence reasons

H2: 1. [Tool Name] - Best for [Use Case]
Brief description (50-75 words)
Key features (bullet list)
Pros/Cons table
Price

[Repeat for each item]

H2: How We Tested
Methodology explanation

H2: FAQ Section
Common questions about the category

Why this works:

• Quick summary = instant AI extraction
• Individual sections = granular citations
• Features list = structured data
• Methodology = authority signal
• FAQ = additional citation opportunities

Citation patterns we’ve seen:

• Quick summary cited for “best X” queries
• Individual sections cited for specific product queries
• FAQ section cited for definition queries

One piece of content, multiple citation opportunities.

Original data content is massively underutilized. Our approach:

Types of data content that get cited:

Why original data wins: AI systems need facts. If you’re the only source of a specific statistic, you get cited.

How we create data content:

1. Survey our audience (even 100 responses works)
2. Analyze our own platform data
3. Compile public data into unique insights
4. Create annual benchmark reports

Example success: We published “2025 Email Marketing Benchmarks” with original data. Result: Cited by AI in 23% of email marketing queries we track.

The investment: One annual report = hundreds of AI citations throughout the year.

Schema markup results from our testing:

A/B test on identical content:

Schemas that matter for AI:

1. FAQPage - Directly feeds AI Q&A extraction
2. HowTo - Structured steps that AI can cite
3. Article - Author, date, publisher info
4. Product - Comparison and recommendation queries
5. Review - Rating aggregation

Implementation tips:

• Use JSON-LD format
• Validate with Google’s testing tool
• Include author schema linked to Article
• Add Organization schema for authority

The compound effect: Schema alone didn’t do much. Schema + good structure + quality content = significant lift.

Here’s a real before/after from restructuring:

Original content:

• 2,500 word guide
• Long paragraphs
• No tables
• Generic H2s like “Learn More” and “Key Insights”
• No FAQ section
• Ranked #4 on Google
• 0 AI citations

Restructured version:

• Same 2,500 words
• Question-based H2s
• 3 comparison tables
• FAQ section with 5 Q&As
• FAQPage schema
• Direct answer in first paragraph
• Still ranks #4 on Google
• 12 AI citations across platforms

What we changed:

1. Rewrote H2s as questions: “Key Insights” → “What Are the Key Benefits of X?”
2. Added direct answers after each H2
3. Created tables from existing list content
4. Extracted common questions into FAQ section
5. Implemented schema markup

Time spent: 4 hours Result: From invisible to regularly cited

Restructuring > Creating new content

Amazing insights. Here’s my complete testing summary:

Top performers in my tests:

1. Comparison tables - 4.2x average citation rate
2. FAQ sections - 3.1x average citation rate
3. Numbered lists with details - 2.4x average citation rate
4. Data with sources - 2.1x average citation rate

Worst performers:

1. Dense paragraphs - 0.3x (below average)
2. Opinion pieces - 0.4x
3. Unstructured long-form - 0.6x

My action plan:

For existing content:

1. Add FAQ sections to top 20 pages
2. Convert relevant content to tables
3. Restructure H2s as questions
4. Implement FAQPage schema
5. Add direct answer summaries

For new content:

• Default template includes FAQ section
• Comparison format for product content
• Tables for any comparative data
• Question-based heading structure

Tools I’m using:

• Am I Cited for tracking citation improvements
• Schema validators
• Content structure auditing

The formula: Question-based H2 + Direct answer + Structured data (table/list) + Schema = AI visibility

Thanks everyone for the data and examples!