Keywords vs. Prompts: What Actually Matters for AI Search Ranking
AI search queries average 70-80 words, not 3-4. Here's how query fan-out changes what you should actually target.
Neither term fully fits, but "prompt" is closer. AI search queries run far longer and more conversational than classic Google searches, and a single question gets broken into multiple sub-queries behind the scenes before an answer is generated. Targeting one short-tail keyword misses most of that sub-query space. Targeting the realistic question a person would actually type into ChatGPT, plus its likely follow-ups, covers far more of it.
- AI search queries average roughly 70-80 words, compared to 3-4 words for a typical Google search.
- One question gets decomposed into multiple sub-queries (query fan-out) before the model synthesizes an answer.
- Headings phrased as actual questions match this pattern directly, which is why it's a scored check, not just a style preference.
- Keyword research isn't obsolete, it's a starting point for scoping a topic, not the end target.
- FAQ content, structured as real Q&A pairs, is the most direct way to be citation-eligible for a specific sub-query.
Why does the old keyword approach undershoot?
Classic keyword research starts with a short phrase ("running shoes for flat feet") and expands outward into variations. That worked because a traditional search query really was that short, a handful of words matched against an index.
AI search queries don't behave like that. They're long, conversational, and specific: "what running shoes should I get if I have flat feet and I'm training for a half marathon on pavement" is a realistic prompt, not an edge case. That single question then gets broken apart by the model into a set of narrower sub-queries, each one researched and answered somewhat independently before being synthesized into the final response.
What does query fan-out actually mean for my content?
If someone asks an AI engine a question in your category, it might silently expand into sub-queries like "best running shoes for overpronation," "half marathon training shoe durability," and "flat feet shoe recommendations reddit," each one a separate retrieval pass. Your page doesn't need to be the single perfect match for the original question, it needs to be citation-eligible for as many of the realistic sub-queries as it can plausibly answer, the same logic behind auditing a page for AI citation readiness in general.
A page optimized for one keyword phrase might match a fraction of a fifteen-word question. A page structured around the actual question, and its obvious follow-ups, matches far more of the sub-query surface a model generates.
How do I actually do this?
Start with the real question, not the keyword. Write down the full sentence a person would type or say to ChatGPT, not the three-word version you'd have typed into Google in 2015.
Phrase your headings as that question, or a close variant. This is directly checkable: our audit's u_question_headings check, documented in the full checklist, looks specifically for whether your section headings are phrased as real questions rather than generic labels, because a heading that already is the question is the strongest possible match for a retrieval system comparing a query against your content.
Add real FAQ content, not decorative FAQ sections. A genuine Q&A pair, marked up as FAQPage schema, is about as directly citation-eligible as content gets: a model can lift the answer without having to infer it from surrounding prose.
Keep keyword research, just change what it's for. It's still useful for scoping what topics exist in a space and what language real people use. Just don't treat the resulting phrase list as the final target, it's an input for building fuller answers, not the deliverable itself.
| Old approach | AI search approach |
|---|---|
| Target a 3-4 word phrase | Target the realistic 15-25 word question |
| Expand outward into keyword variations | Anticipate the sub-queries a model would generate |
| Optimize a single page for one term | Build content that answers multiple related sub-queries |
| Headings as generic labels | Headings phrased as the actual question |
Run your own page's headings and FAQ coverage through the Keywords tab of a full AEO & GEO audit to see exactly which of your headings already match this pattern and which read as generic labels instead.
FAQ
Should I stop doing traditional keyword research entirely?
No, it's still useful for scoping a topic and understanding real language. The shift is in what you do with it: use it to inform the full question you target, not as the final phrase you optimize a page around.
How many sub-questions should one page try to cover?
As many as it can answer genuinely and specifically, usually a handful of clearly related follow-ups. Padding a page with tangential questions just to cover more ground tends to dilute the direct-answer quality that actually gets cited.
Does keyword density still matter at all?
Not in the way it used to. A model isn't counting keyword frequency, it's judging whether your content directly and clearly answers a specific question.