Lecture - 7: Prompt Research: Finding What People Ask AI Tools About Your Topic

Short answer: Prompt research is the practice of discovering exactly what questions real people type into AI tools like ChatGPT, Perplexity, and Google Gemini when searching for topics related to your business. These prompts are often longer, more conversational, and more specific than traditional search keywords. Content written to match these prompts is far more likely to be retrieved and cited in AI-generated answers than content written only for short keyword phrases.

What You'll Learn in This Lecture

• What Is Prompt Research and How It Differs From Keyword Research
• Why AI Prompts Are Longer and More Conversational Than Search Keywords
• How to Find Real Prompts People Use With AI Tools
• How to Use AI Tools Themselves to Discover Prompt Patterns
• How to Use Reddit, Quora, and Forums for Prompt Intelligence
• How to Use "People Also Ask" and Related Questions for Prompt Research
• How to Use Autocomplete and Predictive Search for AI Prompt Patterns
• How to Map Discovered Prompts to Specific Content Opportunities
• How to Prioritize Which Prompts to Target First
• How to Write Content That Matches a Specific Prompt
• How to Track Whether Your Content Is Appearing in AI Answers
• Common Prompt Research Mistakes to Avoid

What Is Prompt Research and How It Differs From Keyword Research

Keyword research finds short phrases (2 to 5 words) that people type into Google. Prompt research finds full questions and multi-sentence instructions that people send to AI tools. The outputs look very different. A keyword might be "best CRM for small business." The AI prompt equivalent might be "I run a plumbing company in Seattle with 8 employees. What CRM should I use to track customer appointments, quotes, and follow-ups? It needs to integrate with QuickBooks."

That longer, contextual prompt reveals far more about the user's real need than a short keyword. Content written to address the specific scenario (plumbing company, small team, appointment tracking, QuickBooks integration) is more likely to be retrieved by AI than generic content about "best CRM software" because it matches the specificity of the prompt more closely.

Prompt research does not replace keyword research. It adds a layer to it. You still need keyword research to understand search volume and traditional SEO opportunities. Prompt research then deepens your understanding of how the same audience formulates requests when using AI tools, which is increasingly a separate and important channel.

Example: A small business accounting software company in Denver, Colorado does traditional keyword research and targets "accounting software for small businesses." They also do prompt research and discover that users ask AI tools: "What accounting software works best for a restaurant with 3 locations that also needs payroll management and tip tracking?" They write a specific guide targeting multi-location restaurants with payroll needs. That guide gets cited in AI answers for that specific scenario, while their generic "best accounting software" page only competes in traditional search.

Why AI Prompts Are Longer and More Conversational Than Search Keywords

When people use a search engine, they have been trained over 25 years to use short, keyword-style queries because search engines historically struggled with natural language. When people use AI chat tools, they write naturally, as if asking a knowledgeable friend. This means AI prompts are routinely 15 to 50 words, include personal context ("I am a first-time homebuyer"), specify constraints ("my budget is under $3,000"), and ask for specific outputs ("give me a step-by-step plan").

This conversational length and specificity has a direct implication for content strategy. A page that only addresses the broad, generic version of a topic will match AI prompts that are similarly broad. A page that goes deep on specific scenarios, specific audiences, and specific constraints will match the longer, more contextual prompts that represent how a growing portion of the population now asks questions.

Conversational prompts also tend to include qualifiers that traditional keywords do not. "Best," "safest," "most affordable," "fastest," and "easiest" all appear frequently in AI prompts along with context about who is asking and why. Content that addresses these qualifiers directly, rather than leaving the answer implicit, is more likely to be retrieved and cited for those specific queries.

Example: A personal injury law firm in Chicago, Illinois targets the keyword "car accident lawyer Chicago" in their traditional SEO. In prompt research, they discover that users ask AI tools: "I was rear-ended on the I-90 in Chicago last week by someone who ran a red light. The other driver's insurance is disputing fault. What should I do first and when should I hire a lawyer?" They write a specific guide for rear-end accident fault disputes in Illinois. Perplexity retrieves it for exactly this type of prompt, putting the firm in front of a high-intent audience no short keyword could have captured.

How to Find Real Prompts People Use With AI Tools

Finding real AI prompts requires looking in several places where people share or discuss how they use AI tools. The most reliable sources of real prompt data are: Reddit communities dedicated to AI tools (r/ChatGPT, r/perplexity_ai, r/AIAssistants), X/Twitter searches for "I asked ChatGPT about [your topic]," YouTube videos and comments where people share AI conversation screenshots, Prompt sharing communities like PromptBase and FlowGPT, and direct testing using the AI tools themselves.

Reddit is particularly valuable because users frequently post their exact prompts alongside the AI's response, often to share something surprising or to ask for help improving the answer. Searching Reddit for "[your topic] + ChatGPT" or "[your topic] + Perplexity" reveals actual real-world prompts with full context about what the user needed and whether the AI answer satisfied them.

Screenshot sharing on social media is another excellent source. When someone shares an AI conversation publicly, they are sharing the exact natural language prompt they used. Following industry communities on LinkedIn and X/Twitter and watching for shared AI screenshots gives ongoing insight into how your target audience formulates requests to AI tools.

Example: A home renovation contractor in San Diego, California searches Reddit's r/HomeImprovement community for "ChatGPT" and "AI" posts. They find 23 threads where homeowners describe using ChatGPT to get renovation advice. Many posts include the exact prompt used. The most common pattern: "I have a 1960s ranch-style home in Southern California and want to update the kitchen for around $15,000. What are the highest ROI changes I can make?" The contractor builds a specific guide for budget kitchen upgrades in older ranch-style homes. It gets cited in AI answers for exactly this type of regional, budget-specific renovation prompt.

How to Use AI Tools Themselves to Discover Prompt Patterns

One of the most efficient prompt research methods is to use AI tools directly. Ask ChatGPT or Perplexity: "What are the most common questions people ask you about [your topic]?" or "What variations of questions do you receive about [specific service or product]?" These responses are not perfect, but they reveal the types of questions and phrasings the AI has been trained on, which directly reflects common user prompt patterns.

You can also prompt AI tools to act as a user in your target audience. Say: "Imagine you are a first-time homebuyer in Austin, Texas with a budget of $400,000 who is confused about mortgage types. What 10 questions would you most likely ask a financial advisor?" The questions generated reflect realistic user prompts in that context and each becomes a content opportunity.

Another technique is to ask an AI tool to help you understand related questions around your core topic. Prompt: "List 20 follow-up questions someone might ask after learning the basics about [your topic]." These follow-up questions reveal the depth of coverage your content needs to address to satisfy the full range of AI prompts your audience generates at different stages of their research journey.

Example: A solar panel installer in Phoenix, Arizona asks ChatGPT: "What are the 15 most common questions homeowners in hot desert climates ask about installing solar panels?" The AI generates questions like "How do extreme summer temperatures affect solar panel efficiency?", "Does Arizona get enough sun for solar to be worth it?" and "How long does the Arizona solar tax credit process take?" The installer writes dedicated sections or articles for each of these questions. Within 3 months, they appear in AI answers for 9 of the 15 specific prompts they identified.

How to Use Reddit, Quora, and Forums for Prompt Intelligence

Reddit, Quora, and niche online forums are living databases of real questions that real people have asked in natural language. Before AI chat tools existed, these platforms were where people went to get human-expert answers to complex questions. The questions people post there closely mirror the prompts they now use with AI tools, because the phrasing is natural, contextual, and specific.

Search Reddit for your topic and sort by "Top" over the past year. Read the highest-voted threads. The original post's question text is often a near-exact replica of an AI prompt a similar user would write today. Quora's "Questions on this topic" sidebar shows related questions in order of search popularity. Both are goldmines for prompt patterns you cannot find in any keyword research tool because these long-form, natural questions never had significant traditional search volume.

Industry-specific forums are especially valuable because they surface the questions that your exact target audience asks. A forum for freelance graphic designers will show you prompts that freelance designers use with AI tools in their specific workflow context. A forum for homebrewing enthusiasts will show prompts that hobbyists use for beer recipes and brewing science. Match your forum research to your exact audience.

Example: An HR technology company in Boston, Massachusetts wants to understand how HR managers use AI tools for their work. They search r/humanresources for posts about ChatGPT and AI tools. They find threads where HR managers share prompts like: "I asked ChatGPT to help me write a PIP (Performance Improvement Plan) for an employee who misses deadlines but has good relationships with clients. Here's what it gave me..." The HR tech company writes a guide: "How to Write a Performance Improvement Plan for Employees With Mixed Performance Records," directly addressing the specific scenario from the Reddit thread. Perplexity begins citing this guide for HR prompt variations around PIPs.

How to Use "People Also Ask" and Related Questions for Prompt Research

Google's "People Also Ask" (PAA) feature and the "Related searches" section at the bottom of search results are excellent proxies for natural language prompt patterns. These questions are real queries that Google's users have typed, often in full-sentence form, and they closely match the type of questions people now ask AI tools.

To extract PAA data systematically, search your main topic on Google and note every PAA question that appears. Click each PAA box to expand it and trigger more related questions. A single topic can generate 40 to 80 PAA questions with enough clicking. Tools like AlsoAsked, AnswerThePublic, and SEMrush's Keyword Magic Tool can export PAA and related question data at scale, giving you hundreds of prompt-style questions for a single topic.

The questions in PAA are particularly useful because they represent gaps: questions Google users asked but felt the existing top results did not answer well enough, so Google inserted the PAA box. These gaps are exactly where AI-optimized content can step in and win the retrieval.

Example: A nutritional supplements company in Salt Lake City, Utah runs PAA research for "creatine monohydrate." They collect 60 PAA questions including: "Is creatine safe for teenagers?", "Does creatine cause hair loss?", "How long should you take a creatine loading phase?", and "Can women take creatine for strength training?" They write individual, focused H2 sections for each of these questions in their comprehensive creatine guide. The guide is subsequently cited in AI answers for 28 of the 60 specific questions they researched, dramatically expanding their AI search footprint for the product category.

How to Use Autocomplete and Predictive Search for AI Prompt Patterns

Autocomplete predictions in Google, Bing, YouTube, and even the AI tools themselves reveal popular prompt patterns. When you type a partial question into these tools, the autocomplete suggestions show the most common ways that query is completed by real users. These completions reflect natural language patterns that closely mirror AI prompts.

Systematically mine autocomplete by testing question starters related to your topic: "how to [your topic]", "what is the best [your topic]", "why does [your topic]", "when should I [your topic]", "how long does [your topic]", "is [your topic] safe / legal / worth it / expensive". Each starter, combined with your topic, generates different autocomplete patterns that reveal different user intent variations.

Perplexity and ChatGPT both show suggested follow-up prompts and related questions after answering. These suggestions are algorithmically generated based on common patterns in real user conversations. Systematically collecting these suggestions while researching your topic inside the AI tools gives you directly sourced AI prompt data.

Example: A dermatology clinic in Miami, Florida mines autocomplete for "retinol" and discovers: "retinol before or after moisturizer," "retinol while pregnant," "retinol and vitamin C together," "retinol percentage for beginners," and "retinol purging how long." Each of these is a specific, natural-language prompt pattern. The clinic writes individual sections for each in their skincare ingredient guide. Within 6 weeks, their guide appears in AI answers for 4 of these specific query patterns, generating new patient inquiries from users who found the clinic through a Perplexity recommendation.

How to Map Discovered Prompts to Specific Content Opportunities

After collecting a large set of prompts, the next step is organizing them into actionable content opportunities. Not every prompt warrants its own article. Some prompts are best answered in a dedicated H2 section within a larger guide. Others reveal the need for an entirely new, standalone article. The decision depends on how specific the prompt is, how much unique content is required to answer it well, and how different it is from topics you already cover.

A useful mapping framework is: if a prompt can be fully answered in 200 to 400 words, it belongs as a dedicated section (H2 or H3) within an existing article. If a prompt requires 800 or more words to answer properly, with examples and sub-points, it warrants its own dedicated page. If a cluster of 5 to 10 related prompts all connect to the same topic, that cluster is evidence of demand for a new pillar page with multiple sub-sections.

Map each prompt to an existing or planned page and track which prompts are currently answered in your content and which are gaps. The gap list becomes your content roadmap. Prioritize gaps where you have genuine expertise and where the prompts represent high-value user intent (someone ready to buy, book, or hire, not just browse).

Example: An immigration law firm in New York, New York collects 80 AI prompts related to U.S. work visas. They map them into groups: 15 prompts about H-1B visas become H2 sections in their H-1B guide. 12 prompts about EB-2 NIW petitions are complex enough to need a dedicated article. 8 prompts about green card timelines become a standalone FAQ page. 5 prompts about O-1 visas for artists become a new pillar article. This mapping gives the firm a 4-month content plan that is entirely driven by real AI prompt demand, not guesswork.

How to Prioritize Which Prompts to Target First

With hundreds of prompts identified, prioritization is essential. The most useful prioritization framework combines 3 factors: relevance to your core business goal, evidence of commercial intent in the prompt, and current gap in your existing content.

Prompts with commercial intent contain language like "which should I choose," "how much does it cost," "is it worth it," "what are the best options," or "I need help with." These prompts represent users closer to making a decision, which means appearing in the AI answer has direct business value. Informational prompts ("how does X work") are valuable for awareness but have longer conversion paths.

Also prioritize prompts where a quick search reveals no good existing answers in AI tools. Test the prompt in ChatGPT or Perplexity right now. If the AI says "I don't have reliable information on this" or gives a vague, generic answer with no specific source cited, that is a high-priority gap where well-written content has an immediate opportunity to fill the void and earn the citation.

Example: A cybersecurity training company in San Jose, California collects 90 prompts about security awareness training. They score each on a 1 to 3 scale: commercial intent (3 = "how do I buy/choose/implement," 1 = "what is"), content gap (3 = AI gives vague answer, 1 = AI gives a full detailed answer already), and business relevance (3 = directly relates to their product). Prompts scoring 7 to 9 out of 9 become their immediate content priorities. The top-scoring prompt: "What security awareness training program is best for a hospital with 500 non-technical staff?" becomes their first new article, written specifically for the healthcare vertical.

How to Write Content That Matches a Specific Prompt

Once you have identified a high-priority prompt, write content that directly mirrors it. Start the article or section with a paragraph that addresses the exact scenario the prompt describes. If the prompt includes context ("I run a plumbing company with 8 employees"), your content should acknowledge that exact user context in its opening, either in a direct statement ("For small plumbing companies with under 10 employees...") or in the framing of the section header.

Use the language of the prompt in your headers and content. If users phrase it as "Is it worth it to...," write a header that says "Is [X] Worth It?" with a direct yes/no answer and supporting detail. If users phrase it as "How do I...," write a header that says "How to..." with numbered steps. Matching the linguistic framing of the prompt increases the retrieval score because the semantic similarity between the prompt and your content is higher.

After the direct answer to the specific scenario, broaden slightly to cover adjacent scenarios in the same section. This layered approach means one well-written section answers the exact prompt with precision and then catches adjacent prompts with slightly different contexts, maximizing the retrieval value of a single piece of content.

Example: A financial advisor in Dallas, Texas identifies the prompt: "I'm 45 years old with $200,000 in a 401(k) and no other savings. Am I behind on retirement savings and what should I do?" They write a section titled "Is $200,000 in a 401(k) at Age 45 Enough for Retirement?" The section opens: "At 45 with $200,000 in a 401(k), most financial planning benchmarks suggest you are behind the commonly cited target of 3x your annual salary, but the gap is very closeable with specific changes. Here is what to do..." The section continues with 5 concrete steps. ChatGPT retrieves and cites this section when users ask this specific retirement savings question, bringing qualified leads directly to the advisor's site.

How to Track Whether Your Content Is Appearing in AI Answers

Tracking AI answer appearances requires a different approach from traditional rank tracking. There is no single tool that tracks AI citation rankings the way Google Search Console tracks keyword positions. Manual testing is currently the most reliable method: run your target prompts directly inside ChatGPT, Perplexity, Google Gemini, and Microsoft Copilot once a month and document whether your content appears in the sources cited.

Tools that are developing AI visibility tracking in 2026 include Semrush AI Toolkit, Ahrefs AI mentions tracking (in beta), and specialized tools like Profound (for AI answer monitoring) and Otterly.ai. These tools vary in coverage and accuracy, but they give a starting point for systematic tracking at scale rather than purely manual testing.

Create a tracking spreadsheet with your target prompts in rows and AI tools in columns. Mark each cell as: Cited (your content is explicitly sourced), Mentioned (your brand is named but not directly sourced), or Not Present. Run this check monthly. Improvements in your citation rate over 3 to 6 months validate that your prompt research and content strategy is working. Stagnation tells you to revisit the content and check for the common mistakes covered at the end of this lecture.

Example: A legal tech company in San Francisco, California sets up a monthly prompt tracking routine. They test 30 prompts across ChatGPT, Perplexity, and Google AI Overviews. In month 1, they appear in 3 out of 90 checks (30 prompts x 3 tools). By month 6, after publishing content targeting each gap they found, they appear in 41 out of 90 checks. This measurable improvement in AI citation rate directly correlates with a 28% increase in inbound demo requests from users who say they "found us through an AI recommendation."

Common Mistakes to Avoid

• Treating prompt research as the same as keyword research and ignoring the longer, conversational nature of AI prompts.
• Skipping the step of actually testing target prompts in AI tools before writing content, missing whether a gap actually exists.
• Writing content that addresses the general topic without acknowledging the specific context embedded in the prompt (industry, budget, location, team size).
• Targeting prompts with no commercial relevance to your business, wasting content production capacity on informational queries that do not drive customers.
• Publishing content and never tracking whether it appears in AI answers, making it impossible to know what is working.
• Ignoring Reddit and forum data and relying only on keyword tools, missing the natural language prompt patterns that only appear in conversational contexts.

Action Checklist

• Run 5 prompts relevant to your business inside ChatGPT and Perplexity right now. Document which brands or sources are cited.
• Search Reddit for your main topic and collect 10 to 20 natural-language questions from post titles and comments.
• Mine Google's PAA boxes for your top 3 topics and collect at least 30 related questions.
• Ask ChatGPT "What are the 15 most common questions people ask you about [your topic]?" and document the list.
• Map your collected prompts to: existing content gaps, new article ideas, and new sections within current articles.
• Score each prompt by commercial intent, content gap, and business relevance. Select your top 5 for immediate content creation.
• Set up a monthly AI citation tracking routine with a spreadsheet of your 20 most important target prompts.

Practice Task

This week, run a full prompt research session for your most important product or service. Use the table below to capture your findings and assign content actions.

Prompt Source	Prompt Discovered	AI Answer Quality Today	Content Action
Reddit	[paste prompt here]	Vague / Partial / Good	New H2 section in existing guide
PAA Research	[paste prompt here]	Vague / Partial / Good	New standalone article
Direct AI testing	[paste prompt here]	Vague / Partial / Good	New FAQ section
Forum post	[paste prompt here]	Vague / Partial / Good	New sub-topic pillar page

Related Lessons Across SEO, AEO, GEO, SEM, and PPC

Use these connected lessons to move through organic search, answer engines, generative AI visibility, paid search, and PPC without losing the bigger strategy.

• Lecture - 1: What Is GEO? How Generative AI Search Works in 2026 (GEO) - return to the course foundation when you need the big picture.
• Lecture 4: Keyword Research Fundamentals (SEO) - connect organic keyword research with the same demand signals.
• Lecture 5: Keyword Research for SEM: Finding High-Intent Search Terms (SEM) - compare organic keyword research with paid search demand.
• Lecture 5: Keyword Research for PPC: Tools, Techniques, and Search Term Reports (PPC) - translate keyword intent into PPC campaign structure.
• Lecture 21: AI Search and Modern SEO (SEO) - connect GEO with the modern SEO shift.

Course Links

• Previous: Lecture 6 - How to Build Brand Mentions and Citations Across the Web
• Next: Lecture 8 - Structured Data and Schema for Generative Engine Visibility
• Run a Free SEO Audit on Your Site

Trusted References

For understanding how natural language queries affect search and AI retrieval, see Google's Helpful Content Guidelines. For prompt sharing communities and research, see the publicly accessible threads on r/ChatGPT and r/perplexity_ai, which contain real user prompts across hundreds of topics.

FAQs

Is Prompt Research Useful If My Business Is Very Local?

Yes, and often more useful than generic keyword research. Local AI prompts are highly specific: "best dentist near downtown Nashville for dental anxiety patients" is the type of prompt a local user sends to an AI tool. Local businesses that target these specific, contextual prompts with dedicated content win local AI citations consistently.

How Often Should I Repeat Prompt Research?

AI usage patterns shift faster than traditional search behavior because AI tools update rapidly. Run a fresh prompt research session every quarter at minimum. Use monthly AI citation tracking to spot new gaps as they emerge. New AI features, model updates, and shifts in how people use these tools create new prompt patterns regularly.

Can I Use the Same Content for Both AI and Traditional SEO?

Yes. Content optimized for AI prompts (direct answers, specific scenarios, structured formatting) also performs well in traditional search for long-tail queries. The techniques are complementary, not competing. Prompt-optimized content tends to rank well for voice search and featured snippets too, since those formats also favor direct, natural language answers.