Prompts and AI queries: how users actually ask
Informational, comparative, transactional: AI queries fall into a few broad families. What each one implies for the content you need to produce.
Users don't ask ChatGPT or Perplexity questions the way they used to type keywords into Google. AI queries map more closely to three broad families — informational, comparative, transactional — and each calls for a different kind of content if you want a chance at being cited.
A conversation rather than a list of keywords
A classic Google search often boils down to two or three words ("invoicing software small business"), because the user knows they'll have to filter the results themselves. Facing a generative AI, the same intent tends to be phrased as a full sentence, with context ("I'm looking for simple invoicing software for a 10-person small business, with automatic accounting export"). This difference has a direct consequence for content: a text that only answers the generic keyword, without addressing contextual variants and precise use cases, captures these longer, more specific queries less effectively.
Informational queries
These are the most common: the user is trying to understand a topic, a concept, or how something works ("How does two-factor authentication work?", "What is free cash flow?"). Unlike a classic Google search, where the user often had to visit several pages to piece together an answer, generative AI condenses information into a single response and expects a source that explains the topic clearly and completely on its own. Content that answers this type of query well is structured like a full explanation, with a clear definition up front — the approach described in the lesson on GEO-ready content.
Comparative queries
The user is trying to choose between several options ("What's the difference between X and Y?", "Which tool should I pick for Z?"). These queries are particularly sensitive for a brand, because the AI has to decide which alternatives to mention and in what order. Content that handles a comparison honestly — laying out decision criteria, the use cases where each option makes sense, without excessively bashing competitors — has a better chance of being seen as a reliable source and cited for the comparison as a whole, not just for the mention of your own product. This is also where the way each engine selects its sources diverges the most, a topic explored in the lesson ChatGPT, Perplexity, Gemini, Claude: how each picks its sources.
Transactional queries
The user is ready to act: sign up, buy, book ("Where to buy this product," "What's the price of this service"). Mainstream generative AI tools today remain more cautious with this type of query than with the previous two, partly because they avoid committing to volatile information like a price or availability. The content most likely to be picked up here presents stable factual information (a public pricing grid, terms and conditions, clear terms) rather than vague sales copy.
What this typology means for your content
This classification isn't just an academic exercise: it should shape your site's editorial structure. A product page generally answers a transactional intent but often fails to address the informational and comparative intents that precede a purchase decision in the user's actual journey. Producing content dedicated to each query type — an explanatory article, an honest comparison, a clear factual page — multiplies the entry points through which a generative AI might cite your brand, at different moments of the user's reasoning. In practice, the same topic often deserves three distinct pieces of content rather than a single article trying to cover everything superficially: depth on each intent matters more than breadth in one piece. It's worth mapping your existing content against these three families before producing anything new: list your current pages, identify which intent each one primarily answers, then spot the intents left uncovered. It's not uncommon to find an entire site structured around transactional intent (product pages, pricing, contact forms) while most of the AI queries asked earlier in the decision process are informational or comparative. Filling these blind spots, rather than multiplying transactional pages, is often the most cost-effective lever in the short term for widening your presence in AI answers.
Anticipating rephrasing
The same information need can be expressed in dozens of different ways depending on the user, their familiarity with the topic, or the engine used. Rather than targeting a single phrasing, it's more effective to cover a subject from several logical angles (definition, method, comparison, limits, concrete cases): this increases the odds that your content semantically matches one of the many possible rephrasings of the same intent. It's also what makes content resilient as query habits evolve — and they're evolving faster with generative AI than they ever did with classic search, as shown by the rapid shift in the research landscape in 2026. Simple tools help identify these rephrasings: related search suggestions, the questions listed in "people also ask" sections of classic search engines, or directly asking the same question to several generative AI tools and comparing the terms they use in their answers. This last method has the advantage of revealing the vocabulary the engines themselves use to synthesize a topic, a useful clue for aligning your own phrasing.
Understanding these three query families doesn't replace directly observing the questions actually being asked about your industry, but it provides a framework for organizing that observation and prioritizing which content to produce first. Revisit the mapping periodically, since the balance between these three intents for a given topic tends to shift as a market matures and buyers become more informed.