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Prompt

FAQ Block Generator (AEO-Optimized)

Turn any article into 5 crisp FAQ Q&A pairs, formatted for FAQPage schema.

1 min readupdated 2026-06-22

/ quick answer

Feed it the full article. Output is ready to paste and to mark up with schema.org FAQPage for AEO wins. Turn any article into 5 crisp FAQ Q&A pairs, formatted for FAQPage schema.

Turn any article into 5 crisp FAQ Q&A pairs, formatted for FAQPage schema. Feed it the full article. Output is ready to paste and to mark up with schema.org FAQPage for AEO wins. Copy the prompt below, swap the bracketed variables for your own context, and run it in any capable model. This prompt node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Context
Feed it the full article. Output is ready to paste and to mark up with schema.org FAQPage for AEO wins.
Prompt
You are an AEO editor. Given an article, produce 5 FAQ pairs that answer questions a reader would actually search for.

Rules:
- Questions phrased as a real user would type/say them (natural language).
- Answers: 40–70 words, self-contained (readable without the article), factually grounded in the article — no invention.
- Cover: 1 definition, 1 how-to, 1 comparison, 1 objection/risk, 1 next step.
- Output JSON: {"faqs": [{"question": "...", "answer": "..."}]}

ARTICLE:
{{article}}
Example Output
{
  "faqs": [
    {"question": "What is RAG?", "answer": "Retrieval-Augmented Generation combines an LLM with a search step..."},
    {"question": "How do I add RAG to my app?", "answer": "Chunk your documents, embed them, store vectors..."}
  ]
}
Related Workflow
Related Tool Stacks
/ frequently asked

What does the FAQ Block Generator (AEO-Optimized) prompt do?

Feed it the full article. Output is ready to paste and to mark up with schema.org FAQPage for AEO wins.

Which AI models work with this prompt?

It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.

What output should I expect?

{ "faqs": [ {"question": "What is RAG?", "answer": "Retrieval-Augmented Generation combines an LLM with a search step..."}, {"question": "How do I add RAG to my app?", "answer": "Chunk your documents, embed them, store vectors..."} ] }.

/ topics#seo#aeo#prompting
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