QAPage Schema for AI Question-Answer Content (2026)

QAPage schema marks user-generated question-and-answer content for AI extraction. Unlike FAQPage (which is for publisher-controlled Q&A), QAPage is designed for community content where users submit both questions and answers—think Stack Overflow, Reddit, Quora, or forum discussions. AI systems increasingly cite community Q&A sources, making QAPage implementation valuable for platforms hosting user-generated expertise.

This guide covers when to use QAPage, implementation requirements, and optimization for AI citations.

QAPage vs FAQPage: When to Use Which

The two schema types serve different content patterns.

Schema selection guide:

Scenario	Use QAPage	Use FAQPage
Single author writes both Q&A	No	Yes
Users submit questions	Yes	No
Multiple answers per question	Yes	No
Voting/ranking on answers	Yes	No
Community/forum content	Yes	No
Static FAQ sections	No	Yes

Key differences:

FAQPage Schema:
├── Publisher controls both questions and answers
├── One answer per question
├── Static, authoritative content
└── Common on brand websites, help pages

QAPage Schema:
├── Questions submitted by users
├── Multiple answers possible
├── Answers ranked by community
└── Common on forums, Q&A sites, communities

Using the wrong schema type creates confusion. AI systems expect specific patterns from each.

QAPage Schema Structure

QAPage schema includes the question and one or more answers with metadata.

Core structure:

{
  "@context": "https://schema.org",
  "@type": "QAPage",
  "mainEntity": {
    "@type": "Question",
    "name": "The question text",
    "text": "Expanded question with context",
    "answerCount": 3,
    "upvoteCount": 45,
    "dateCreated": "2026-01-10",
    "author": {
      "@type": "Person",
      "name": "User Name"
    },
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "The accepted/best answer",
      "dateCreated": "2026-01-11",
      "upvoteCount": 28,
      "author": {
        "@type": "Person",
        "name": "Answerer Name"
      }
    },
    "suggestedAnswer": [
      {
        "@type": "Answer",
        "text": "Alternative answer",
        "upvoteCount": 12,
        "author": {
          "@type": "Person",
          "name": "Another User"
        }
      }
    ]
  }
}

Property Reference

QAPage includes several properties AI systems evaluate.

Question properties:

Property	Required	AI Benefit
name	Yes	Query matching, extraction
text	Recommended	Additional context
answerCount	Recommended	Completeness signal
upvoteCount	Optional	Popularity signal
dateCreated	Recommended	Freshness evaluation
author	Recommended	Attribution capability

Answer properties:

Property	Purpose	AI Relevance
text	The answer content	Primary extraction source
upvoteCount	Community validation	Quality signal
dateCreated	Answer freshness	Recency evaluation
author	Who answered	Expertise attribution

AcceptedAnswer vs SuggestedAnswer

QAPage distinguishes between accepted and suggested answers.

Answer type hierarchy:

Answer Extraction Priority:
├── acceptedAnswer (highest priority)
│   └── Marked by question author or community as best
│
├── suggestedAnswer with high votes (medium priority)
│   └── Alternative quality answers
│
└── suggestedAnswer with low votes (lower priority)
    └── Additional perspectives

AI behavior with answer types:

Answer Type	AI Citation Likelihood
acceptedAnswer	Highest - treated as definitive
Top suggestedAnswer	Medium - alternative viewpoint
Other suggestedAnswer	Lower - supplementary info

Marking an acceptedAnswer tells AI which response to prioritize. Without it, AI must infer from upvotes.

Implementation for Forums and Communities

Adapt QAPage to different community content types.

Platform implementations:

Platform Type	Implementation Approach
Technical Q&A (Stack Overflow style)	Full schema with code blocks in answers
Discussion forums	QAPage for threads with clear questions
Product communities	QAPage for support questions
Knowledge bases	Consider FAQPage unless user-submitted

Forum thread example:

{
  "@context": "https://schema.org",
  "@type": "QAPage",
  "mainEntity": {
    "@type": "Question",
    "name": "How do I fix WordPress white screen of death?",
    "text": "My WordPress site shows a blank white page after updating plugins. I've tried clearing cache but it's still white.",
    "answerCount": 5,
    "upvoteCount": 23,
    "dateCreated": "2026-01-08",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Disable plugins by renaming the plugins folder via FTP: 1) Connect to your server via FTP. 2) Navigate to wp-content. 3) Rename 'plugins' to 'plugins_old'. 4) Refresh your site. If it loads, re-enable plugins one by one to find the conflict.",
      "upvoteCount": 31,
      "dateCreated": "2026-01-08"
    }
  }
}

Optimization Tactics for AI Citations

Maximize AI visibility with QAPage optimization.

Content optimization:

Tactic	Implementation
Clear question titles	Phrase questions as users search them
Comprehensive answers	Include complete solution, not just hints
Structured answer text	Steps, lists, or clear paragraphs
Upvote accuracy	Reflect actual community voting
Fresh content	Update dateCreated when answers edited

Answer text formatting:

High AI extraction potential:
"To fix the issue: 1) Clear your browser cache. 
2) Disable conflicting plugins. 3) Check your PHP 
version. These three steps resolve 90% of cases."

Lower AI extraction potential:
"Try looking at the documentation, it might help.
Also some users have mentioned plugins could be 
an issue but I'm not 100% sure about that."

Specific, actionable answers extract cleanly. Vague suggestions don't cite well.

Common QAPage Mistakes

Avoid errors that reduce AI visibility.

Mistakes and fixes:

Mistake	Problem	Solution
Using FAQPage for forums	Wrong schema type	Switch to QAPage
No acceptedAnswer	AI can't identify best response	Mark community-chosen answer
Outdated upvoteCounts	Trust signals misaligned	Sync with actual votes
Missing author info	Attribution gaps	Include author for both Q and A
Answer text too long	Extraction truncation	Lead with key information

Validation Requirements

Test QAPage implementation before relying on it.

Validation checklist:

Google Rich Results Test - confirms valid markup
Schema.org validator - syntax verification
Check acceptedAnswer displays correctly
Verify answerCount matches actual answers
Confirm date formats are ISO 8601 compliant

Key Takeaways

QAPage schema for AI search optimization:

QAPage is for user-generated Q&A - Use FAQPage for publisher-controlled content instead
AcceptedAnswer signals the best response - AI prioritizes marked answers over suggestedAnswer
Multiple answers are expected - Include suggestedAnswer for alternative solutions
Community signals matter - UpvoteCount influences AI quality assessment
Clear question titles improve matching - Phrase questions as users would search
Answer text should be actionable - Specific solutions extract better than vague guidance
Validation is essential - Test with Rich Results Test before publishing

For platforms hosting community Q&A content, QAPage schema creates the machine-readable structure AI systems need to accurately extract and cite user-generated expertise.

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Matt Pru