ChatGPT search queries are getting longer: optimise for the real question

A prospect who used to type “CRM software price” into Google now asks ChatGPT a completely different question: “What is the best CRM for a fifty-person marketing agency, with Salesforce integration and a price under 150 euros per user per month?” Same person, same need, but a totally different way of searching. Where classic search behaviour revolves around short fragments and a string of follow-up queries, people in AI assistants formulate one rich, conversational question with all the context already baked in.

That difference in length and phrasing is not a detail. It determines whether your B2B content gets found and cited in AI answers or not. Anyone who keeps optimising for two or three word keywords misses exactly the long, intent-rich questions buyers use to start their decision process today. In this article I explain how big that length gap really is, why it happens, and how to align your content with natural full sentences in practice.

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How much longer are AI search queries really?

The numbers leave little room for doubt. An analysis by marketing agency Nectiv of more than 8,500 ChatGPT prompts, reported by Search Engine Land, shows that a ChatGPT search query is 5.48 words long on average and that 77% of all queries are five words or longer. For comparison: classic Google searches hover around three words, often loose fragments without a verb or context.

According to data from SimilarWeb, ChatGPT prompts are even roughly seventeen times longer than an average Google search, measured over the period October 2023 to September 2025. The exact ratio differs per study and per measurement method, but the direction is the same everywhere: AI search queries are substantially longer and more conversational.

For B2B this matters more than for most consumer markets. Buying decisions are complex, involve multiple criteria and often several stakeholders. Exactly that kind of multi-layered question lends itself perfectly to an AI assistant, and that is exactly where your chance to appear as a source sits.

Why people search differently in AI

The length gap is no coincidence. For twenty years a search bar has trained people to think in keywords: the shorter, the better the odds of a hit among the ten blue links. An AI assistant, by contrast, feels like a conversation. People write the way they speak, with complete sentences, conditions and nuances.

On top of that, users expect a synthesis straight away from an AI, not a list to filter themselves. As Semrush describes, people no longer type fragments like “CRM software price” and then run several follow-up searches. They ask the full question right away, including budget, company size and integration requirements. The intent that used to be spread across a series of short searches is now condensed into one long prompt.

For you this means the search intent is far more explicit than in classic search. That is a gift: you have to guess less about what someone actually wants. Anyone who reads the search intent behind those long questions properly can write content that matches the need exactly instead of a generic keyword.

Long prompts get broken up: query fan-out

Here sits the second layer marketers often miss. An AI system does not literally search for your long sentence. It breaks it into several underlying searches and queries each of them separately. That mechanism is called query fan-out. Ahrefs describes it as a technique where AI search platforms automatically expand one user question into several related sub-questions in order to generate a more complete answer.

The longer and more specific the prompt, the more sub-questions the model can derive from it. That CRM question can, for instance, break down into sub-questions about Salesforce integrations, about per-user pricing models, and about CRMs suited to mid-sized agencies. Each of those sub-questions is a separate chance to get cited, even if your page would never rank for the original full sentence.

This fundamentally changes the logic of content planning. Instead of aiming at one head keyword, you aim at the cluster of sub-questions a long prompt triggers. Exactly how that decomposition and classification process works, and how to align your content with it, you can read in our article on query fan-out and intent classification.

What this means for your B2B content

The practical conclusion is that content written for short keywords structurally underperforms in AI search results. You need content that answers natural, complete questions in the language your audience uses itself. In concrete terms:

• Write in question-and-answer form. Use headings that are real questions (“What does a CRM cost for a fifty-person agency?”) and give a concise, citable answer right underneath.
• Work context and conditions into your text. Company size, industry, budget and integrations are exactly the elements that recur in long prompts. Name them explicitly.
• Cover the sub-questions. One long prompt fans out into several sub-questions; make sure your page or cluster answers each of them separately.
• Speak your buyer’s natural language. Avoid jargon where your client would use plain words, and vice versa.

This dovetails neatly with a long-tail strategy. Long, specific questions are long-tail by definition: less volume per question, but far less competition and far higher intent. In our guide on long-tail keywords for B2B we explain how to map those specific questions systematically and turn them into content.

How to find and use these questions

The good news: you do not have to invent those long questions. They are findable. Start with your own sales and support team, because the questions prospects ask them are often word for word the prompts they later type into an AI. Add the “people also ask” questions in Google, the suggestions of the AI assistants themselves, and your B2B keyword research where you explicitly filter on question forms and long variants.

Collect those questions, group them by topic and by intent, and build content around them that phrases every answer as clearly and factually as possible. That is immediately the basis for becoming a reliable source AI systems are happy to refer to. How to structure your content so models actually cite you, you can read in becoming source material for AI. Beyond reliability, demonstrable experience counts too; how to prove experience signals so AI recognises real experience is covered separately.

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