Open your keyword tool and search for your main service. You will see a clean list of short phrases with monthly volumes. Now open ChatGPT or Perplexity and watch how a real buyer asks the same question. It is longer and more specific, with context attached.
That gap is one of the biggest changes behind AI SEO. This article explains how prompts differ from keywords, why AI systems search differently from the way users type, and how to adapt your research without throwing away what already works.
Prompts Are Longer, but Be Careful With the Numbers
Industry studies agree on the direction, but their figures vary because they measure different things. A Semrush analysis, as reported by Topify, found the average ChatGPT prompt runs about 23 words, against 4.2 words for a typical Google search, with Google’s AI Mode sitting between them at 7.22 words.
The detail that often gets lost: that 23-word figure applies to prompts when web search isn’t activated. For prompts that do trigger search, the same clickstream analysis found length nearly doubled from 4.7 to 8.7 words between early 2025 and early 2026. Both statements can be true.
The practical lesson is to avoid quoting a single headline number. What matters is that people increasingly include context and constraints that a keyword never carried.
The Hidden Layer: Query Fan-Out
When someone asks an AI engine a question, the system rarely searches their exact words. It breaks the question into several sub-queries, runs them, and combines what it finds. Google described this explicitly when it launched AI Mode, calling it a “query fan-out technique.”
AirOps research gives a sense of the scale for ChatGPT. In 89.6% of searches, ChatGPT expanded the original prompt into two or more fan-out queries before deciding what to cite. The same report found that 32.9% of cited pages appeared only in search results for a fan-out query, not the starting prompt. Seer Interactive reported that 95% of Gemini’s fan-out queries had zero monthly search volume by traditional metrics. Back to Reports +2
Read that last point twice. The searches shaping AI answers often do not appear in your keyword tool, because they are generated by the machine, not typed by a person. Another analysis noted that ChatGPT’s fan-out queries share only 13% word overlap with the original prompt, so they can explore related concepts your page never names.
These figures come from AI-visibility vendors, so treat them as directional evidence, not settled fact. Fan-out behavior also changes as models update. One source notes that a later model release changed the picture substantially.
What This Changes in Practice
None of this makes keyword research obsolete. It means keywords tell you what humans type, while fan-out reveals what the AI looks for after reading the human. You now need both layers.
It also does not mean you should publish a page for every imaginable sub-query. Google has said that site owners do not need to capture every long-tail variation. Depth on a topic beats a sprawl of thin variations.
A Five-Step Prompt Research Method
- Collect real prompts. Mine sales calls, support tickets, chat transcripts, on-site search and comment threads. Customers describe their situation in full sentences, and those sentences are your prompt set. Do not rewrite them into keywords.
- Sort prompts by intent. Group them into definitions (“what is…”), comparisons (“X versus Y for…”), recommendations (“best option for a team of ten with a small budget”) and how-to questions. Each type wants a different kind of passage.
- Pull out the constraints. A prompt like “CRM for a 15-person agency, cheap, works with Gmail” carries four constraints. Constraints shift which brands appear, so build content that speaks to specific situations, not generic categories.
- Reverse-engineer the fan-out. Run each prompt in a few engines and note which sources are cited. Where tools show the sub-searches, record them. Look for angles your content never covers, such as pricing, alternatives, integrations or risks.
- Map gaps to sections, not necessarily new pages. Add a clearly headed section that answers each missing sub-question directly. The aim is passages an AI can lift cleanly. AirOps also found that pages with 50% or more title-query overlap were cited at 20.1%, against 9.3% for pages below 10% overlap. Clear, descriptive titles seem to help, though this is a correlation from one dataset.
How to Write for Fan-Out Without Writing for Robots
- Answer first. Begin each section with a direct, plain answer, then add nuance.
- Use descriptive headings. “How much does X cost for a small team?” works better than “Pricing.”
- Be specific. Name the conditions under which your advice holds. Specific claims are easier to match to specific prompts.
- Show your evidence. Numbers, dates, examples and sources make a passage quotable and checkable.
- Stay human. If a section reads like keyword filler, an editor, a reader and a model will all see it.
How Sarkar SEO Applies This
At Sarkar SEO, we build the prompt set before we touch the keyword list. Sarkar SEO interviews sales and support teams, gathers the exact wording customers use, and then checks which sources the major AI engines cite for those prompts.
The result is usually a short list of missing sections, not a hundred new pages. Sarkar SEO then writes or revises content around those gaps and re-tests the same prompts each month, so progress is measured against how real buyers ask. We adapt the prompt set by market and language, because buyers in different countries phrase the same need differently.
