Question Research for AEO

TL;DR

AI search is question-driven. Build a list of 50+ candidate questions per topic from real user data (search consoles, AI suggestions, Reddit, support tickets), classify them by intent, prioritize by volume × business relevance, and ship one well-structured page per primary question with related questions as H2/H3 sections.

For broader context, see the /aeo hub and How to Write AI-Citable Answers.

Why question research matters

Classic SEO targets keywords; AEO targets questions. The unit of optimization is the question and its extractable answer, not the noun phrase. AI systems route users to the source that most clearly answers a specific intent — which means your content has to map to a specific question, in language users actually use.

This shift has two practical consequences. First, the unit of work is the question-and-answer pair, not the page; one page can host many. Second, the most valuable inputs to research are the places where humans actually phrase questions in their own words — not search-volume reports.

Question research methods

1. AI platform mining

Ask AI systems directly for related questions:

• "What questions do people ask about [topic]?"
• Use Perplexity's "Related" suggestions on a seed question.
• Watch for "People also ask" variants in Google AI Overviews.
• Note the follow-up questions ChatGPT or Claude offers after an initial answer; these are real expansion paths users take.

2. Traditional keyword tools (with a question filter)

3. Community mining

Real questions live where humans actually ask things:

• Reddit threads in your niche (the title is usually the question)
• Quora questions and the better-voted answers
• Stack Overflow for technical topics
• Customer support tickets and chat transcripts
• Sales call recordings and discovery questions

4. AI-search-specific signals

A few signals only matter for AI search and are easy to miss with classic SEO tooling:

• Citations to competitors in Perplexity for your seed question (their headings are direct evidence of what gets extracted).
• Follow-up suggestions inside ChatGPT and Claude after a baseline question — these are user-intent expansions you can target.
• The exact phrasing inside Google AI Overviews answer cards; matching that phrasing as an H2 often improves extraction.
• Forum or Discord answers you find linked from AI Overviews; they reveal the surface area AI considers "answer-grade."

Question categorization framework

Prioritization matrix

A simple scoring approach: score each factor 1-3, sum, and rank. Anything scoring 12+ is a high-priority page; 9-11 goes into a backlog; below 9 is generally not worth a dedicated page.

Question-to-content pipeline

1. Collect 50+ candidate questions per topic.
2. Categorize each by intent (definition, comparison, etc.).
3. Prioritize by volume × business relevance.
4. Cluster related questions by primary entity.
5. Plan one page per primary question; related questions become H2/H3.
6. Write answer-first content (see How to Write AI-Citable Answers).
7. Validate by asking the target question in 3+ AI systems and observing extraction.

Worked example: "llms.txt"

Seed topic: llms.txt. Pull from GSC, Perplexity related, and Reddit/r/SEO.

Result: one definition page, one how-to, one comparison, one evaluation, with the list and conditional handled as H2/H3 sections inside related pages.

Validating your answers

After publishing:

1. Ask the target question verbatim in ChatGPT.
2. Ask the same question in Perplexity and inspect citations.
3. Check Google AI Overviews for the question.
4. Note: Are you cited? Was the extracted snippet your intended answer?
5. Iterate on structure (heading text, answer length, schema) when extraction is wrong.

A reasonable cadence is to re-test priority questions monthly across at least three platforms (ChatGPT, Perplexity, Gemini), and to log citation status alongside the question in a tracking sheet. AI answer surfaces shift quickly; pages that win one month can lose the next, and visible regressions are usually fixable with small structural edits before they become traffic problems.

FAQ

Q: How many questions should one page target?

One primary question, with 2-5 closely related secondary questions as H2/H3. Pages that try to answer too many questions tend to lose extractability.

Q: Where do AI systems get their list of questions?

A mix of training data, web crawl, real-time search, user follow-ups, and PAA-style derivation. There is no single "AI keyword tool" — triangulate from multiple sources above.

Q: Do question pages need both an FAQ section and an article body?

Often yes. The body answers the primary question deeply; the FAQ section catches related questions and qualifies for FAQPage schema, which improves citation eligibility.

Q: How do I find questions specific to my industry?

Customer support tickets and sales-call transcripts are the highest-signal source — they contain the exact phrasing your audience uses, including questions they would never type into Google.

Q: What is the simplest first step?

Export the last 90 days of GSC queries, filter rows containing a question word (what, how, why, when, vs), sort by impressions, and audit your top 20 against the categorization framework above.

Q: How often should I refresh my question list?

Quarterly is a reasonable default for stable topics; monthly for fast-moving ones (AI tooling, regulation). Re-pull GSC and AI-platform suggestions, drop questions whose volume or relevance has decayed, and add anything new from support tickets and sales calls.