30 ChatGPT SEO Prompts to Boost AI Search Citations

Classic keyword research surfaces what people search on Google. AI-era keyword research adds the prompts buyers ask chatbots, which are longer, more conversational, and more question-shaped. These five surface both.

• 1. Buyer-question expansion. "List 30 category-defining questions a [BUYER PERSONA] asks a chatbot when researching [PRODUCT CATEGORY]. Phrase each as a complete sentence ending in a question mark."
• 2. Long-tail variant generator. "For the seed term [SEED KEYWORD], generate 20 long-tail variants combining [INTENT MODIFIERS: best, vs, alternatives, for, in 2026, how to, what is]. Format as a table: keyword, intent classification, modifier type."
• 3. Competitor citation gap. "Given my product [PRODUCT DESCRIPTION] and these 5 competitors [COMPETITOR LIST], list 20 category questions where my competitors are likely cited but I am not. For each, suggest the page format that would close the gap."
• 4. Comparison query mining. "Generate 25 'X vs Y' queries that combine my brand [BRAND NAME] with the competitors a buyer is most likely to compare us to in [CATEGORY]. For each, note who currently wins in ChatGPT and why."
• 5. Pain query surfacing. "List 15 problem-shaped queries (starting with 'why is', 'how do I fix', 'my [thing] isn't') that a buyer of [PRODUCT] types into ChatGPT. These are the highest-CVR queries because they signal acute pain."

A good brief tells the writer what to write and how to structure it for AI extraction. These prompts produce briefs that engines like ChatGPT, Perplexity, and Gemini cite cleanly.

• 6. Definition-led brief generator. "Write a content brief for the keyword [TARGET KEYWORD]. The brief must include: a one-sentence definition under the H1, 6 H2 outlines, a comparison table structure, 5 FAQ questions, and 3 cited statistics from named sources."
• 7. Search-intent classification. "Classify the search intent of [KEYWORD] (informational / commercial / transactional / navigational) and recommend the optimal content format (listicle / definition / step-by-step / comparison / case study). Justify the choice in two sentences."
• 8. Outline expansion. "Take this rough outline [OUTLINE] and expand each H2 into 3 H3 subheads, each phrased so a reader can tell what they will learn before reading the body."
• 9. Competitor structural audit. "Visit [COMPETITOR URL] and reverse-engineer the structural elements an AI engine would extract: H1, H2 count, FAQ block size, table rows, citation density. Suggest 3 structural changes my version should make to outperform it."
• 10. E-E-A-T brief checklist. "Generate an E-E-A-T compliance brief for content about [TOPIC]. Specify the named-author byline requirement, the lived-experience evidence to include, the named sources to cite, and the year-stamped data points the article must reference."

Most pages are written for human scanners. AI engines extract differently. These prompts audit whether a page is structured to be cited, not just read.

• 11. Extractable-sentence audit. "Read [PAGE URL] and identify the 10 sentences most likely to be quoted verbatim by ChatGPT, Perplexity, or Gemini in an answer. Score each on a 1-5 scale for citation likelihood and explain why."
• 12. Definition extraction test. "Pretend you are ChatGPT answering 'what is [TOPIC]?'. Identify the single best sentence to extract from [PAGE URL]. If no clean candidate exists, write the sentence the page should add."
• 13. FAQ surfacing. "List 8 questions a buyer of [PRODUCT] asks a chatbot. For each, score whether [PAGE URL] answers it cleanly. Note the gaps."
• 14. Comparison table extraction. "Build a 4-column comparison table for [PRODUCT CATEGORY] including my product [BRAND] and competitors [COMPETITOR LIST]. Columns: Tool, Key feature, Pricing, Best for. Format so each row stands alone when extracted."
• 15. Citation-likelihood score. "Score [PAGE URL] on citation likelihood across ChatGPT, Perplexity, Gemini, and Google AI Overviews. For each engine return: score 1-10, top blocker, recommended fix."

FAQ blocks and structured data are the highest-leverage editorial changes for AI search visibility. These prompts produce the markup engines can parse cleanly.

• 16. FAQ block generator. "Generate 8 FAQ questions and answers for a page on [TOPIC]. Each question must use a real buyer phrasing. Each answer must be 40-80 words, lead with a definition, and end with a clear fact a chatbot could quote."
• 17. FAQPage JSON-LD. "Convert this list of [N] questions and answers into a valid FAQPage JSON-LD block. Output only the JSON-LD, no prose."
• 18. Article schema. "Generate an Article JSON-LD block for [PAGE TITLE]. Include headline, datePublished, dateModified, author with url, publisher, image, and articleSection. Use today as datePublished."
• 19. HowTo schema. "Generate a HowTo JSON-LD block for a step-by-step guide on [TASK]. Include 6 HowToStep entries, each with name, text, and url anchor."
• 20. Schema audit. "Read [PAGE URL] and identify which schema types it should have but doesn't (Article, BreadcrumbList, FAQPage, HowTo, Product, Review). For each missing type, generate the JSON-LD."

Knowing where competitors are cited and you aren't is the fastest path to a useful content backlog. These prompts surface that gap.

• 21. Engine-by-engine gap. "For each of these 20 prompts [PROMPT LIST], note which competitors [COMPETITOR LIST] are cited in ChatGPT, Perplexity, Gemini, and AI Overviews. Mark the prompts where my brand [BRAND] should be cited but isn't."
• 22. Reddit citation mining. "Find the 10 Reddit threads most likely to be cited by Perplexity when answering questions about [PRODUCT CATEGORY]. For each, suggest the kind of contribution (answer, AMA, case study) that would surface my brand."
• 23. YouTube citation mining. "List 8 YouTube videos that currently get cited in Google AI Overviews for [TOPIC]. For each, identify the transcript element that earned the citation. Suggest a YouTube content piece I could ship that would compete for the same citation."
• 24. Competitor brief reverse-engineer. "Read [COMPETITOR PAGE URL]. Reverse-engineer the brief that produced it: target keyword, intent, target length, citation density, FAQ count, schema choices. Suggest 3 ways my version could outperform it."
• 25. External citation pitch list. "Identify the 5 highest- authority third-party sites in [INDUSTRY] where a guest post or contribution would earn AI citation lift for [BRAND]. For each, draft a 3-sentence pitch outline."

Citations to old content decay as fresher sources surface. These prompts identify pages that need a refresh and produce the structural changes that reverse the decline.

• 26. Decay detection. "Given this list of pages and their citation share over the last 90 days [DATA], identify the 5 pages with the steepest decline. For each, suggest the most likely cause (stale data, format shift, competitor displacement)."
• 27. Refresh brief. "Generate a refresh brief for [PAGE URL]. Specify: stats to update with year stamp, sections to restructure for AI extractability, FAQ questions to add, dateModified bump, and 3 external mentions to re-pitch."
• 28. Year-stamp pass. "Read [PAGE URL] and identify every date reference. Suggest the year-stamped replacement for each (e.g., 'recent studies' → '2026 ConvertMate benchmark')."
• 29. Statistic refresh. "List every statistic in [PAGE URL] with its source. Identify the 5 that need updating and suggest May 2026 or later sources for each."
• 30. Competitor displacement audit. "For [PAGE URL] and this query [QUERY], identify which competitor is currently displacing me in ChatGPT and Perplexity. Reverse-engineer the structural choices that earned their citation and recommend the changes my page needs."