FAQPage Schema

FAQPage schema is structured data markup that identifies question-and-answer content on a web page, wrapping each Q&A pair in machine-readable JSON-LD format. It's one of the highest-impact schema implementations for AI citation — pages with correct FAQPage schema are cited approximately 2x more frequently for Q&A queries compared to identical content without the markup.

AI systems value FAQPage schema because it explicitly labels which text is a question and which is the answer, eliminating ambiguity in content parsing. When ChatGPT, Perplexity, or AI Overviews encounter a page with FAQPage schema, they can extract specific Q&A pairs with high confidence that the passage is a direct answer to a specific question. Without schema, AI must infer question-answer relationships from content structure — a less reliable process.

Implementation requires wrapping each question-answer pair in the FAQPage JSON-LD structure. Each Question entity contains the question text (name) and an Answer entity with the answer text (text). Multiple Q&A pairs are listed in the mainEntity array of the FAQPage object.

Best practices for FAQPage schema: include 5-8 Q&A pairs per page (enough for comprehensive coverage without diluting relevance), write answers that are self-contained (make sense without reading the question — because AI may extract the answer alone), include specific data in answers (numbers, dates, comparisons), and ensure schema content exactly matches visible page content. Schema that contains different text than what users see violates Google's guidelines and can result in schema being ignored entirely.

FAQPage schema also generates FAQ rich results in traditional Google search, providing a dual benefit: enhanced SERP appearance and increased AI citation rates.