What people actually ask AI assistants — and how to find the questions that trigger citations.
People do not talk to AI assistants the way they talk to search boxes. They ask full, conversational questions — with context, constraints, and follow-ups — rather than typing three compressed keywords. That difference matters, because the prompt is what the engine decomposes and retrieves against. Prompt research is the discipline of finding out what your audience actually asks AI systems, and it has a useful property: the prompt tells you almost exactly what passage you need to write.
Someone using a search engine types the minimum that might work: a few words, stripped of grammar, optimised for a system they expect to match text. Someone using an AI assistant writes a question, often a long one, with context about their situation and what they are trying to decide. The same underlying need produces a terse keyword string in one interface and a full sentence in the other.
This matters because the phrasing shapes retrieval. A conversational prompt carries far more signal about intent, constraints, and context, and engines decompose that richer input into sub-questions. Content written to match compressed keyword strings can therefore miss what conversational prompts actually ask. Understanding why prompts differ from keywords is why prompt research is a distinct discipline rather than a rewording of keyword research — the input is genuinely different in kind.
Prompt research is the practice of discovering the actual questions people pose to AI assistants about your domain, and using them to direct content. Where keyword research asks what terms people search, prompt research asks what questions people ask — in full, natural phrasing, with the context and qualifiers real users include. The output is a set of real prompts you can target with content designed to be the cited answer.
The discipline is newer and less tooled than keyword research, which means it relies more on assembling evidence from several sources than on querying a single database. But its purpose is the same: ground content decisions in observed demand rather than assumption. Understanding what prompt research is establishes its role alongside keyword research — the two together describe how your audience seeks information across both search and assistants.
Real prompts can be sourced from several places. People Also Ask boxes surface the question-shaped queries users pose around a topic. Community forums and discussion platforms contain questions asked in full natural language, often with the context that makes them realistic. Your own support tickets and sales conversations capture the questions customers actually ask, phrased as they actually phrase them. And the engines themselves reveal prompt patterns through their suggested and follow-up questions.
The practical method is to gather from several of these sources, since each captures a different slice: public question boxes give breadth, community discussion gives authentic phrasing and context, and internal sources give the questions of people who are already your audience. Understanding where to find real prompts is the core practical skill of the discipline, since no single tool supplies them the way keyword tools supply search terms.
The AI engines are themselves a research source. Posing questions in your domain and observing what the engine returns reveals which sources it currently cites, how it frames the answer, what sub-questions it addresses, and what follow-up questions it suggests — each of which is evidence about how the engine decomposes and answers queries in your subject.
This direct observation is particularly valuable because it shows the competitive reality: who currently owns each answer, and what kind of content is being drawn on. It also surfaces the adjacent questions the engine considers related, which expands your prompt map. Understanding that engines can be mined directly is why hands-on observation is part of the method — it supplies evidence about actual engine behaviour that no third-party tool fully replicates.
Raw prompt lists contain enormous variation in phrasing for what is often the same underlying need. Ten differently-worded questions may all be asking the same thing, and treating them as ten targets would produce ten redundant pages. Clustering prompts by the user’s underlying job — what they are actually trying to accomplish — collapses that variation into a manageable set of real needs.
The practical method is to group prompts by what would satisfy them: if the same well-written passage would answer several prompts, they belong together. This produces a set of distinct jobs, each of which can be owned by one strong piece of content. Understanding clustering by job is what turns a sprawling prompt list into an actionable plan, and it mirrors the intent-grouping discipline that keyword research requires for the same underlying reason.
A conversational question carries its own answer specification — the scope, the constraints, the form. Own each high-value prompt with a self-contained answer, and you have written precisely what retrieval is looking for.
The most practically useful property of prompt research is that a well-captured prompt tells you what to write with unusual precision. A conversational question states its own scope and constraints: what is being asked, under what conditions, and what form of answer would satisfy it. Reading the prompt carefully therefore yields a specification for the passage that would answer it — the claim to make, the qualifications to include, the length that fits.
This is a genuine advantage over keyword targeting, where a compressed string leaves the required content largely to inference. With a real prompt in hand, you can write the passage that answers exactly that question, self-contained enough to be lifted. Understanding that the prompt specifies the passage is why prompt research connects so directly to execution — the research output is nearly a content brief already.
To own a prompt is to have content that answers it so directly and completely that an engine retrieving for that question finds your passage the obvious thing to use. In practice this means a clear heading matching the question, an immediate self-contained answer beneath it, and supporting detail after — the answer-first structure covered in its own piece, applied prompt by prompt.
Self-containment is the critical property: the passage must make sense lifted out of its surroundings, since that is how it will be used. A passage that depends on preceding paragraphs for its meaning is harder to extract and less likely to be cited. Understanding what owning a prompt requires is why prompt research and answer-first writing are so tightly coupled — the research identifies the question, and the writing discipline makes your answer the liftable one.
Prompt research tells you what to target; DUNkē tells you whether it worked. Track which prompts you’re cited for across eight AI engines — against competitors — so your prompt map becomes a measured programme.
Not all prompts deserve equal effort, and prioritising them uses similar logic to keyword prioritisation with some adjustments. Commercial relevance matters most: prompts posed by people making decisions in your category are worth more than casually adjacent ones. Competitive position matters next: prompts where competitors are cited and you are not represent clear opportunity, while prompts nobody owns well may be easier to claim.
Frequency matters too, though it is harder to measure than search volume, which is why evidence from multiple sources helps establish which questions are genuinely common. The practical exercise is to score prompts on business value and current competitive position, then work the high-value gaps first. Understanding prompt prioritisation prevents the research from producing an undifferentiated list, and connects it directly to the gap-analysis discipline covered separately.
Because assistants are conversational, a single question is rarely the whole interaction. Users follow up: narrowing, comparing, asking about edge cases, or shifting to an adjacent concern. This means the practical unit of research is often a conversation shape rather than an isolated prompt — the opening question and the follow-ups that typically succeed it.
The practical implication is to map the natural progression around your key prompts and ensure your content answers the follow-ups as well as the opener. Content that answers only the opening question serves the start of the conversation while competitors are cited through the rest of it. Understanding that prompts come in sequences is why prompt research should capture conversational arcs, which connects directly to optimising for conversational surfaces.
Prompt research does not replace keyword research; the two describe different behaviours by the same audience. Keywords reveal how people search when they expect to choose from results; prompts reveal how they ask when they expect an answer. Most organisations need both, because their audience uses both interfaces, often for different stages of the same decision.
In practice the disciplines share considerable ground: both seek real demand, both require grouping by underlying intent, and both feed one content plan. The difference is in sourcing and phrasing, and in what the output specifies. Understanding how they fit together is why modern research should run them in parallel rather than choosing between them — the same content, well-planned, can serve a keyword and the conversational prompt expressing the same need.
The consolidated output of prompt research is a prompt map: the significant questions your audience asks in your domain, clustered by job, prioritised by value and competitive position, and matched to the content that owns or should own each. It functions as a content plan and, once you are measuring citations, as the tracking list against which visibility is assessed.
The map should be a living document, since the questions people ask evolve as products, categories, and understanding change. The practical routine is to refresh it periodically from the same sources, adding emerging questions and retiring those that no longer matter. Understanding the prompt map as the deliverable is what gives the discipline a concrete output — a prioritised list of questions you intend to be the cited answer for.
The recurring mistakes mirror keyword-research errors in new clothing. Treating prompts as keywords — stripping them to their core terms — discards the context and phrasing that make them useful. Targeting every phrasing variation as a separate page produces redundancy. Guessing at prompts rather than sourcing real ones anchors content to assumption. Ignoring follow-ups leaves you present only at the start of conversations. And building a map without measuring citations leaves you unable to tell whether any of it worked.
The remedies follow: preserve full phrasing, cluster by underlying job, source prompts from real evidence, map conversational arcs rather than isolated questions, and pair the map with citation measurement. Because the discipline is young, these errors are common and avoiding them is genuinely differentiating. Understanding the failure modes is what keeps prompt research grounded in observed behaviour rather than drifting into keyword research with longer strings.
Prompts are more useful when captured with their context rather than reduced to bare text. A useful record includes the full question as asked, the situation or constraint the asker mentions, where it was observed, what stage of a decision it implies, and which engine or platform it came from. That surrounding information is what makes the prompt actionable later.
Reducing prompts to their core terms discards precisely the signal that distinguishes prompt research from keyword research. A question that specifies a constraint tells you what qualifications your answer must include; one that reveals urgency tells you what form of answer serves. Understanding what a good prompt record contains is why the capture format matters — a spreadsheet of stripped phrases is a keyword list wearing longer strings.
Prompts recur in recognisable shapes across domains: comparison prompts weighing options, suitability prompts asking whether something fits a described situation, procedural prompts asking how to accomplish something, troubleshooting prompts describing a problem, and validation prompts checking a conclusion the asker has already reached.
Recognising the pattern tells you what form of answer will satisfy it: comparison prompts need explicit criteria and honest treatment of alternatives, suitability prompts need clearly-stated conditions, troubleshooting prompts need diagnostic structure. The practical use is to classify your prompt set by pattern and check that your content offers the right shape of answer for each. Understanding prompt patterns is what turns a list of questions into a content specification.
Comparison prompts — asking which option is better, or how alternatives differ — are commercially valuable and awkward for brands, because the honest answer sometimes favours a competitor in particular situations. Content that claims superiority in every case reads as marketing, and engines drawing on corroborated sources tend to prefer material that treats alternatives fairly.
The practical resolution is to be genuinely useful about fit: state clearly what you are best for and, where true, what other options suit better. This is more citable precisely because it is more credible, and it attracts the prospects who actually fit. Understanding the honesty problem in comparison prompts is why the instinct to claim universal superiority tends to cost citations rather than win them.
The questions people ask assistants shift more quickly than search vocabulary does, because conversational phrasing tracks how people are currently thinking about a subject and because assistant capabilities change what people think to ask. A prompt map built a year ago will be partially stale in a way a keyword list of the same age typically is not.
The practical implication is a shorter refresh cycle: revisiting the prompt map more frequently than keyword research, watching for new question shapes appearing in communities and support channels, and retiring prompts that no longer reflect how people ask. Understanding that prompts change faster is why prompt research should be a running practice rather than a periodic project, and why the map needs an owner.
A prompt map is immediately useful as an audit instrument, before any new content is written. Taking each high-value prompt and asking whether any existing page answers it directly, in a liftable passage, usually reveals that much of the needed substance already exists but is buried, scattered, or phrased differently from how people ask.
The resulting work is often restructuring rather than creation: adding a question-shaped heading, moving an existing answer into the opening position, making a passage self-contained. This is fast and high-yield compared with writing new pages. Understanding prompts as an audit tool is why the map should be applied to existing content first — the cheapest citations are usually the ones already almost earned.
Prompt sets skew toward the questions teams find interesting, which usually means early-stage informational ones. But people use assistants throughout a decision: exploring a problem, comparing approaches, evaluating specific options, checking a choice before committing, and troubleshooting after. Each stage produces distinct prompts with distinct commercial value.
Later-stage prompts are typically fewer, less frequent, and considerably more valuable, since the asker is close to acting. The practical discipline is to deliberately cover the full progression rather than allowing the map to fill with early-stage questions. Understanding the funnel dimension is why prompt research should be audited for coverage across decision stages, not merely for volume of questions collected.
The final step that makes prompt research operational is converting the map into content briefs. Each high-value prompt becomes a specification: the question as the heading, the required answer with its necessary qualifications, the evidence needed to make it credible, the format the question implies, and the follow-ups the same page should address.
This is unusually direct compared with keyword-derived briefs, because the prompt already states most of what the brief needs to say. The practical output is a queue of well-specified writing tasks rather than a list of topics requiring interpretation. Understanding how to turn the map into briefs is what completes the discipline — research that stops at a list of questions leaves the hardest translation work undone.
The tooling for prompt research is immature, which leads some teams to defer the work until better instruments exist. That is unnecessary, because the highest-value sources require no tooling at all: support tickets and sales-call recordings contain the questions your customers actually ask, phrased naturally, with the context that makes them realistic — and most organisations already have both in quantity.
A workable manual programme is to review a sample of recent customer questions monthly, record the recurring shapes, supplement them with People Also Ask data and community observation, and pose the resulting priority questions to the main engines directly to see who is currently cited. This takes hours rather than weeks and produces a usable map. Understanding that the discipline works without dedicated tooling is why waiting is the wrong response — the evidence is already sitting in systems you own.
Because prompt research often draws on small samples, a validation step is worth building in before committing significant content investment to a question. Useful checks include whether the question appears across several independent sources rather than one, whether posing it to an engine produces a substantive cited answer — indicating the engine treats it as a real question — and whether it connects to something commercially meaningful.
A question appearing in a single support ticket may be genuinely representative or may be idiosyncratic, and the difference matters when the response is a substantial piece of content. The practical rule is to require corroboration from at least two independent sources before treating a prompt as a priority. Understanding validation is what keeps the prompt map anchored to real demand rather than to whichever question the last person happened to notice.
People ask AI assistants full, conversational questions rather than typing compressed keywords, which makes prompt research a distinct discipline from keyword research rather than a variation on it. Real prompts are assembled from People Also Ask boxes, community discussion, support tickets, sales conversations, and direct observation of the engines themselves — then clustered by the user’s underlying job so that variation in phrasing collapses into a manageable set of real needs.
The discipline’s useful property is that the prompt specifies the passage: a well-captured conversational question states its own scope and constraints, telling you almost exactly what to write. Own each high-value prompt with a self-contained answer — question as heading, complete answer immediately beneath — map the follow-ups as well as the openers, prioritise by business value and competitive position, and measure which prompts you are actually cited for.
“People search in keywords and ask in questions. The question is the more useful artefact — it carries its own answer specification, telling you exactly which passage to write.” The Age’X Research Team
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