AI search lets people describe a whole situation in one prompt instead of compressing it into three keywords, and the systems behind it split that prompt into several sub-searches before answering. To be included, a page has to answer one specific facet of a situation clearly enough to be pulled out on its own. As of 14 October 2026, Google says there is no special markup for this; the work is in content depth and structure.
Key Takeaways
- Long prompts reveal the constraints behind a purchase or decision (budget, injury, family size, timeline), so pages need to answer situations, not just categories.
- Google's own documentation says AI Overviews and AI Mode may run several related searches (query fan-out) and show a wider set of supporting links than classic search.
- There is no extra schema or "AI file" required; a page must be indexed and snippet-eligible, per Google's AI features guidance.
- Write each page as a set of self-contained facet answers, each with a heading that mirrors a real sub-question.
- A single tracked prompt shows one version of an experience; personal context changes answers, so pair prompt tracking with analytics on landing pages.
- Third-party coverage (communities, specialist publications, creators) helps AI decide when your brand fits a particular situation.
What actually changed: from keywords to situations
On 12 October 2026, Search Engine Land published a piece by Becky Simms of Reflect Digital arguing that AI has not made people's needs more complicated; it has let them express complexity that was always there. Her example contrasts a compressed query like "best family hotel Tenerife" with a long prompt about room layout, food, car rental and pool needs. The article contains no survey numbers, and its examples are hypothetical, so treat it as an informed argument, not a dataset.
The practical point holds up anyway. A keyword used to tell you the topic. A long prompt tells you the topic plus the reason. That reason used to surface later, in a sales call or a support chat. Now it arrives at the very first touchpoint, and the AI decides which sources can satisfy it.
Google's mechanism for this is documented. In its AI features and your website guide, Google says AI Overviews and AI Mode may use a "query fan-out" technique, issuing multiple related searches across subtopics and data sources while generating a response. The result, Google says, is a wider and more diverse set of supporting links than a classic results page. When AI Mode was first announced in March 2025, TechCrunch described the same idea: several related searches run at once, then combined into one response with follow-ups available.
If you want the mechanics in more depth, I have a separate explainer on the glossary side: see the AEO glossary for the terms used here, and what Google AI Mode is for the product itself.
Longer prompts carry the constraints that used to appear only in a sales conversation.
Anatomy of a multi-part prompt
Take a prompt a shopper might plausibly write: "I'm 38, training for a half marathon, I had a knee issue last year, I have wide feet and a budget around 10,000 rupees. What should I buy and how should I train the first month?"
An AI system can break that into facets such as:
- Running shoes for wide feet
- Cushioning and stability considerations after a knee issue
- Options within a price band
- A beginner half-marathon first-month plan
Each facet can be answered by a different source. Your page does not need to answer all four. It needs to be the clearest answer to one of them, and ideally be internally linked to pages that answer the neighbours.
Facets, not keywords
Aleyda Solis's write-up of Google's query fan-out approach makes the same point: complex, multi-criteria and comparative questions trigger extensive fan-out, while simple factual ones trigger little or none. Her suggestion is to brainstorm the sub-questions around each broad topic and give focused answers to each. Her example sub-queries are illustrative, produced by her in a lab version of AI Mode, so do not read them as Google's actual internal queries.
The five types of constraint to cover
When I review a page for this, I look for whether it handles the five kinds of constraint that show up in long prompts.
| Constraint type | Example in a prompt | What your page needs |
|---|---|---|
| Person | "wide feet", "first-time buyer", "non-technical founder" | A section naming who the product or advice is for and not for |
| Money | "under 10,000 rupees", "free plan only" | Explicit price bands, what is included at each |
| Time | "before December", "in 30 days" | Realistic timelines and lead times |
| Context | "after a knee issue", "migrating from Shopify" | Conditions, prerequisites, caveats |
| Comparison | "or should I go with X" | A fair comparison with criteria |
If a page only has a feature list, it fails most of these. Adding a short "Who this is for" and "Who should pick something else" block is usually the cheapest improvement.
A worked template: the facet page
Here is the structure I use when rewriting a broad page into one that survives fan-out.
- Direct answer (2-3 sentences). State the recommendation and the main condition it depends on.
- Situations table. Rows are common situations; columns are recommendation, why, and a caveat.
- One H2 per facet. Each H2 is phrased like the sub-question. Each section opens with a one-sentence answer, then evidence.
- Evidence block. Your own data, test notes, or first-hand experience. Attribute anything borrowed.
- Limits. What you do not know, what varies, when to see an expert.
- Next questions. Three links to the pages that answer the likely follow-ups.
That last block matters because AI Mode and Overviews support follow-up questions. Google's guidance lists internal links among its supporting practices, and linking the likely next question gives both users and crawlers a path.
One broad question, several narrower facets: each facet is a chance to be the cited source.
Do not chase every long-tail prompt
A trap here is trying to write a page for every imaginable prompt. That is the old long-tail keyword mistake with new packaging, and it drifts toward the thin, scaled pages that Google's spam policies target. I covered the spam angle in my content audit after the spam update.
A better rule: write for situations you can speak to with real knowledge. If you cannot add a fact, a number, a test, or a judgement that is not already on ten other pages, skip it.
Measuring when prompts are personal
Simms notes that AI assistants may use context from past conversations, and that memory and personalisation vary by platform and user settings. That has a measurement consequence: the same prompt can produce different answers for different people. So a standard tracked prompt shows only one version of the experience.
What I do instead:
- Keep a small prompt panel for trend lines (see my 50-prompt panel method), but label it as a sample.
- Check which landing pages receive referrals from AI platforms in analytics, and note which increasingly specific pages grow.
- Watch what answers leave out. If AI answers about your category never mention a constraint your product handles well, that is a content gap.
- Use sales calls, support tickets and community threads as the real source of long-prompt language.
Google also notes that AI feature traffic is reported inside the "Web" search type in Search Console, so you will not see a separate AI Mode bucket there as of this writing, per the AI features guide above.
Third-party proof decides "who fits"
Relevance in a long prompt is personal: "best" depends on the person. Simms recommends niche focus plus coverage from specialist publications, influencers and communities so AI can tell when a brand is the right choice. The same theme runs through Kevin Indig's 12 October analysis for ecommerce, where he cites Ahrefs correlations showing YouTube and branded web mentions correlating more strongly with AI Overview visibility than Domain Rating (as reported by Indig; I have not re-run that study). His full piece is on Growth Memo, with the shopping-behaviour study at Growth Memo's high-stakes decisions piece, and a Search Engine Land version.
For a service business or an edtech brand, the translation is simple: be present in the places where your specific kind of customer describes their situation. For a follower-growth example of building that kind of presence, I did social work with Masai School, where Instagram grew from 26K to 117K and LinkedIn from 50K to 200K; I disclose that because it is my own work, and it is about audience, not AI citations.
A 7-day plan
- Day 1: Pull 20 real long questions from support, sales calls, Reddit and reviews. Paste them verbatim.
- Day 2: Split each into facets. Mark which facet you already answer well.
- Day 3: Pick your five most valuable pages. Add a "Who this is for / not for" block.
- Day 4: Add a situations table to each.
- Day 5: Rewrite H2s as the sub-questions people actually ask.
- Day 6: Add three next-question internal links per page.
- Day 7: Set a baseline: referral sessions from AI platforms by landing page, plus your prompt-panel snapshot.
What not to do
- Do not add FAQ schema hoping it triggers fan-out; Google says no special markup is needed.
- Do not stuff paragraphs of prompt-style text into pages.
- Do not publish AI-generated answers for situations you have no knowledge of.
- Do not read one tracked prompt as the truth.
For the model context behind this, Google rolled its newest model into AI Mode for paid subscribers on 13 October, as reported by Search Engine Roundtable; the fan-out logic applies regardless of model, but expect behaviour to shift as models change.
FAQ
What is query fan-out in simple terms?
Query fan-out is when an AI search system turns one question into several related searches across subtopics and data sources, then combines the results into one answer. Google's documentation says AI Overviews and AI Mode may use it. It is why a single long prompt can cite several different pages.
Do I need special schema to appear in multi-part AI answers?
No. Google's AI features guidance says there are no additional requirements or special optimisations beyond being indexed and eligible to show with a snippet. Keep structured data accurate to visible content, but do not expect it to unlock fan-out inclusion.
Are longer prompts really becoming common?
The Search Engine Land piece argues the shift qualitatively and offers no statistics. I would not quote a percentage without a primary source. What you can verify yourself is whether your own query reports and support logs show longer, more situational questions.
How is this different from long-tail SEO?
Long-tail targets a specific phrase. Multi-part answering targets the situation behind several phrases at once, and the AI decides which facet to pull from which source. The risk is writing thin pages per variation, which is the thing to avoid.
Should I write one long page or many short ones?
Write one substantial page per situation, with clear sections per facet, rather than many near-duplicate pages. Link to neighbouring situation pages. If two pages would answer the same facets, merge them.
How do I find the long questions people ask?
Support tickets, sales call notes, reviews, community threads and internal site search. Copy the wording as written. These are better than keyword tools for this purpose because they preserve constraints.
Can I track whether a long-prompt page is working?
Partly. Track AI-platform referral sessions by landing page in analytics and watch whether specific pages grow. Search Console folds AI feature traffic into the Web performance data, so isolate it with care.
Does personalisation make prompt tracking useless?
Not useless, but limited. Treat a tracked prompt as one sampled view. Combine it with referral data and with manual checks from a couple of different accounts or contexts.
Does this apply to non-Google AI tools?
The behaviour of splitting and combining is common across AI search products, but the exact implementation differs and is rarely documented. Google's guidance is the only primary source I have cited here; for other tools, test directly.
What is the fastest improvement to an existing page?
Add a "Who this is for and who should choose something else" block and a situations table. It is quick, it is useful to humans, and it speaks directly to the constraints in long prompts.
Want help applying this?
If you would like to see my work or talk through your own content, visit younusfardeen.in and use the contact form. I bring 4+ years of marketing experience across organic growth, SEO and AEO, and I am happy to look at where your pages stand against the situations your customers actually describe.