Prompt research is the practice of discovering the questions and conversational prompts people type into AI assistants like ChatGPT, Perplexity, and Google AI Overviews, so you can shape content and ads that show up in answers. Keyword research captures short search phrases typed into Google; prompt research captures full, context-heavy prompts that carry constraints, budgets, and buyer situations.
If you plan AI ads or content without knowing how buyers actually phrase their prompts, you target the wrong conversations and your spend lands next to irrelevant answers. Get prompt research right and you can place ChatGPT ads and supporting pages where high-intent buyers are asking, instead of guessing which phrases matter.
Within a ChatGPT Ads and AI search program, prompt research is the input step that maps the real conversations your buyers have with answer engines before they choose a product. It gathers the full prompt text people use, the context they include, and the intent stage behind each question. The output is a library of prompts you can target, answer, and track across platforms.
A useful prompt research set captures more than phrasing. For each prompt you record the exact wording, the persona and situation, the funnel stage from awareness to purchase, and what the buyer is optimizing for, such as price, integrations, or compliance. On ChatGPT, advertisers supply natural-language context hints that describe the conversations where an ad belongs, so your prompt library becomes the raw material for those hints.
Prompt research sits next to keyword research and audience research without replacing either. Keyword research still feeds search campaigns, and audience data still shapes who you reach, while prompt research defines the questions AI answers. AirOps supports this work by surfacing the prompts buyers ask across AI engines, search queries, People Also Ask, and community forums.
Resources: turn real buyer questions from your sales calls and support tickets into tracked prompts.
Prompt research runs as a repeatable loop that moves from raw buyer language to tracked, targetable prompts.
Collect sources: Pull real questions from sales calls, support tickets, community forums, and keyword tools so you start from language buyers actually use.
Draft prompts: Rewrite those inputs as full conversational prompts, keeping the constraints, context, and desired output buyers include.
Classify intent: Tag each prompt by funnel stage and topic so you know which ones signal buying intent and which build awareness.
Prioritize: Rank prompts by demand and competition, and flag the high-intent ones worth targeting with ChatGPT ad context hints or dedicated content.
Track and refresh: Run the priority prompts across AI platforms on a schedule, watch whether you get mentioned or cited, and add new prompts as buyer language shifts.
The output tells you which conversations your brand can realistically win and where competitors already own the answer. It does not tell you the exact prompt volume, because AI prompt counts shift with personalization and multi-turn chats, so treat the numbers as relative tiers.
Prompt research decides where your AI ad budget and content effort actually land. In ChatGPT Ads you cannot bid on prompts directly; you supply context hints and the system matches your ad to conversations it judges relevant. Without a researched prompt library behind those hints, you are describing your audience from memory, and the match quality suffers.
It aligns spend with real demand: When your context hints are built from prompts buyers actually use, your ads surface in high-intent conversations instead of unrelated ones.
It exposes a concrete failure mode: Loose or guessed targeting is why ads misfire, like a clothing retailer appearing under a dating-apps question; researched prompts tighten the match and cut wasted impressions.
It builds a durable content map: The same prompt library tells your content team which questions to answer on owned pages, so paid and organic AI visibility reinforce each other.
SEO managers use prompt research to find the conversational questions AI engines answer in their category and map them to pages worth creating or refreshing.
Content strategists use prompt research to prioritize topics by buyer intent, so each article answers a prompt real buyers type into ChatGPT or Perplexity.
Growth marketers use prompt research to build the context hints behind ChatGPT ad campaigns and aim spend at high-intent commercial prompts.
In ChatGPT Ads, context hints are the natural-language descriptions of conversations, topics, and keyword-style phrases you give the system so it can match your ad to relevant prompts, since advertisers cannot bid on individual prompts the way they bid on Google keywords.
Every prompt maps to a funnel stage from awareness through consideration, evaluation, and purchase, and sorting your prompts this way tells you which conversations deserve paid ad spend, which deserve educational owned content, and which are not worth chasing yet.
Because AI prompt counts shift with personalization, multi-turn conversations, and differences between platforms, prompt research reports relative demand tiers instead of exact monthly volumes, which keeps you prioritizing the prompts that matter without trusting precision the data cannot support.
Target ChatGPT ad context hints with the exact prompts buyers use instead of guessed phrases.
Surface high-intent commercial prompts across ChatGPT, Perplexity, and Google AI Overviews.
Prioritize content topics by real buyer questions and funnel stage.
Reduce wasted ad impressions by tightening how your hints match conversations.
Build one prompt library that serves both paid campaigns and organic AEO.
Track whether AI answers mention or cite your brand on the prompts you care about.
Start from real buyer language in sales calls, tickets, and forums, because invented prompts miss the constraints buyers actually include.
Write full prompts with context and desired output, since AI matches ads on whole conversations.
Tag every prompt by intent stage, so you can route spend to commercial prompts and content to earlier ones.
Keep context hints specific to the conversations you can win, because broad hints trigger irrelevant placements.
Refresh your prompt set on a schedule, since buyer language and AI answer behavior shift quickly.
Connect prompts to pages and campaigns, so research turns into action instead of a static list.
Avoid treating prompt research as a one-time keyword export. The common mistake competent teams make is scraping a generic prompt list once, loading it, and never revisiting it, which leaves your targeting anchored to language your buyers have already moved past.
AirOps: AirOps Prompt Discovery surfaces the prompts buyers ask across AI engines, search queries, People Also Ask, and forums, then tracks whether your brand gets cited on them.
Google Search Console: Google Search Console shows the real queries and People Also Ask patterns that seed conversational prompts for your pages.
Semrush: Semrush helps you expand seed topics into question and long-tail keyword clusters you can rewrite as full AI prompts.
List your buyer questions: This week, pull a couple dozen real questions from recent sales calls, support tickets, and your own inbox. Write each one in the buyer's words, with the context they gave.
Rewrite them as prompts: Turn each question into a full conversational prompt, keeping constraints like budget, team size, and existing tools so it matches how people actually ask ChatGPT.
Classify and prioritize: Tag each prompt by topic and intent stage, then flag the high-intent commercial prompts that justify ad spend first.
Set up tracking: Add your priority prompts to an AI visibility tool and run them across ChatGPT, Perplexity, and Google AI Overviews to see where you already appear.
Act on the gaps: For prompts where you are missing, write context hints for ChatGPT ads and brief content for owned pages, then recheck your visibility after a few weeks.
Prompt research is the practice of finding the real prompts buyers type into AI assistants so you can target and answer them.
You do it by collecting real buyer language, rewriting it as full prompts, tagging intent, and tracking results across AI platforms.
The main constraint is that AI prompt volumes are not precise, so prioritize with relative demand tiers.
The main risk is loose targeting, where guessed prompts place your ads beside irrelevant conversations.
The biggest advantage is one researched prompt library that powers both ChatGPT ad context hints and owned content.
Prompt research and keyword research both start from what your audience wants, but they capture it at different levels of detail. Keyword research records the short phrases people type into a search box, usually two or three words stripped of context. Prompt research records the full conversation someone has with an AI assistant, including the constraints, budgets, team size, and desired output they build into a single question. That difference matters because AI answer engines respond to the whole prompt, so a buyer who types best CRM into Google might write a paragraph describing their stack and requirements in ChatGPT. The practical upshot is that keyword research still guides search campaigns and page targeting, while prompt research tells you which detailed questions AI engines are answering and whether your brand shows up in those answers. Most teams run both and feed keyword data into their prompt library as one input among several.
Refresh your prompt research on a regular cadence and avoid treating it as a one-off project. A practical rhythm for most teams is a light review each month and a deeper rebuild each quarter, with faster checks whenever you launch a product, enter a new market, or notice your ad placements drifting. The reason for the cadence is that buyer language changes, AI platforms change how they answer, and new competitors enter the conversation, so a prompt list that was accurate in spring can be stale by autumn. Between full refreshes, add prompts as they surface in sales calls and support tickets so your library keeps growing with real demand. If you track prompts in a dedicated tool, let its weekly recommendations flag new questions worth adding. The signal that you have waited too long is simple: your tracked prompts stop matching the questions your sales team hears every week.
Your prompt research varies across platforms because each AI assistant answers, cites, and personalizes differently. ChatGPT, Perplexity, Gemini, and Google AI Overviews draw on different sources, apply different ranking logic, and handle follow-up questions in their own way, so the same prompt can return your brand on one platform and omit it on another. Personalization adds more variance, since a logged-in user's history and settings influence what they see, and multi-turn conversations reshape the context before an answer is generated. Results also shift between runs on a single platform, because these systems are probabilistic and do not repeat the same answer every time. For prompt research, the lesson is to track each priority prompt on every platform you care about and watch the trend across several runs instead of reading a single result as truth. Treat per-platform differences as a map of where to focus, since a gap on one engine is often a clear opportunity.
Not directly, and it helps to be clear about why. In ChatGPT Ads you cannot bid on a specific prompt the way you bid on a Google keyword; instead you supply context hints that describe the conversations, topics, and audiences where your product fits, and the system decides which prompts to match your ad against. Prompt research does not give you a switch to force placement, but it does give you the raw material that makes your hints accurate, which is the strongest input you actually control. When your context hints are built from the real prompts and constraints buyers use, the matching system has clearer signals and tends to place your ad in more relevant conversations. So the honest answer is that you influence placement indirectly: research the prompts, write precise hints from them, watch which conversations your ad lands in, and refine the hints over time. Expect to steer the match through better signals, and treat tighter hints as the way you gain ground over time.
Good prompt research is judged less by the size of your list and more by how well it reflects real demand and drives action. A strong set is grounded in the actual language buyers use, pulled from calls, tickets, and forums instead of a generic export, and every prompt carries a persona, an intent stage, and a note on what the buyer is optimizing for. Coverage is balanced across the funnel, so you are not only chasing bottom-funnel commercial prompts while ignoring the awareness questions that shape how buyers frame the category. A healthy library is also connected to work: each high-priority prompt maps to a context hint, a page, or a brief, and you track whether your brand gets mentioned or cited over time. As a rough benchmark, if your sales and support teams recognize the prompts as questions they hear every week, and your tracked coverage is improving run over run, your prompt research is doing its job.