Skip to content 
Learn how to read Google Search Console’s generative AI performance report, track AI Overviews visibility and measure AI traffic in GA4.
Fan-out queries are one of the biggest reasons AI search feels different from traditional Google search. Instead of matching one user query to one ranked list of pages, systems such as Google AI Mode can break a question into several sub-questions, search across those subtopics at the same time, and combine the results into one answer.
That changes how SEO works.
It also explains why people are now talking about GEO, or Generative Engine Optimisation.
As of 24 August 2026, Google’s own AI Mode help page says AI Mode uses a “query fan-out” technique that divides a question into subtopics and searches each one simultaneously across multiple data sources.
What Are Fan-Out Queries?
Fan-out queries are the hidden sub-queries an AI search system creates from one user prompt. The system breaks the main question into smaller search tasks, retrieves information for each one, then synthesises the results into a single answer.
For example, a traditional search for:
“best plumber in Melbourne for emergency repairs”
may focus heavily on pages that match that exact phrase.
An AI search system may treat the same prompt as a bundle of needs:
- Which plumbers serve Melbourne?
- Which ones offer emergency repairs?
- Which have strong local reviews?
- Which mention fast response times?
- Which show pricing transparency?
- Which are licensed or insured?
- Which are available after hours?
- Which pages provide trustworthy contact details?
That is the important shift.
The literal keyword is no longer the whole game.
How Query Fan-Out Works in AI Search
Google says AI Mode can use query fan-out to break a question into subtopics and issue many queries at the same time on the user’s behalf. Google’s AI Mode announcement described this as breaking a question into subtopics and issuing a “multitude of queries simultaneously”.
In simple terms, the process looks like this:
User asks one complex question
↓
AI system identifies subtopics
↓
Multiple related searches run in parallel
↓
Results are retrieved from different sources
↓
The system compares and synthesises answers
↓
User receives one combined response
This is different from the older mental model of SEO.
Traditional SEO often starts with one target keyword and one page.
Fan-out search starts with one user need and many implied questions.
Why Fan-Out Queries Matter
Fan-out queries matter because your page may be discovered through a sub-question, not the original user prompt. This means content needs to answer the surrounding topic, not only the exact keyword.
A page may not rank first for “best emergency plumber Melbourne”.
But it may still be used by an AI answer if it clearly answers:
- emergency response time
- service area
- 24/7 availability
- licensing
- transparent call-out fees
- common repair types
- verified reviews
This rewards content depth.
It also rewards clear page structure.
A Practical Example: Emergency Plumber Search
Let’s use the prompt:
best plumber in Melbourne for emergency repairs
A query fan-out AI system may generate something like this:
The AI answer may then combine results from service pages, local listings, reviews, pricing pages, FAQs and business profiles.
That means one thin service page is not enough.
A stronger page would include:
- clear emergency service coverage
- suburb or area names
- response-time expectations
- licence or qualification details
- pricing notes or call-out fee policy
- common emergency issues
- review themes
- FAQs
- contact options
- internal links to related services
The page becomes useful because it answers the cluster behind the query.
What Fan-Out Queries Mean for SEO
Fan-out queries mean SEO must move from single-keyword targeting to topic-complete answering. Ranking for the exact phrase still matters, but AI search can also retrieve pages that answer the implied sub-questions behind the main prompt.
This does not make keywords useless.
It changes how keywords should be used.
A target keyword should be treated as the entry point into a topic, not the full brief.
Old SEO Thinking
The old model often looked like this:
Keyword → Page → Ranking → Click
The page was built around one primary term.
Supporting keywords were often added as variations.
AI Search Thinking
The AI search model looks more like this:
User prompt ↓ Sub-queries ↓ Retrieved evidence ↓ Synthesised answer ↓ Citation, mention or click
The page now needs to be useful as evidence.
That means clear sections, complete answers and strong topical coverage.
What Is GEO in Marketing?
GEO, or Generative Engine Optimisation, is the practice of improving content so it can be retrieved, cited, mentioned or used by generative AI systems such as AI Overviews, ChatGPT, Gemini, Perplexity and Copilot.
The term became more widely discussed after researchers introduced Generative Engine Optimisation as a framework for improving visibility in generative engine responses.
GEO is not only about ranking a URL.
It is about whether an AI system can understand, trust and use your content in a generated answer.
What Is GEO in Marketing?
GEO in marketing means structuring your content, brand presence and supporting evidence so generative AI systems can retrieve and cite your business when answering user questions. It focuses on visibility inside AI-generated answers, not only blue-link rankings.
A GEO-friendly page usually has:
- direct answers
- clear headings
- strong topical coverage
- factual consistency
- credible sources
- original examples
- structured data where relevant
- clear entity signals
- internal links
- quotable explanations
These are also good SEO practices.
That is why SEO and GEO are converging.
SEO vs GEO: What Actually Changes?
SEO and GEO are closely related, but they measure different outcomes.
SEO focuses on improving visibility in search engine results pages.
GEO focuses on improving visibility inside generated AI answers.
Difference Between SEO and GEO
The difference between SEO and GEO is the target environment. SEO optimises for ranked search results, while GEO optimises for generative answer systems that retrieve, summarise and cite sources.
But GEO does not replace SEO.
Google’s AI Mode and AI Overviews still depend on web retrieval and search quality systems. Google also says AI Mode searches across multiple sources and provides links where users can explore further.
The foundation still matters:
- crawlable pages
- technical SEO
- helpful content
- authority signals
- internal links
- topical depth
- clear answers
GEO sits on top of that.
It does not erase it.
How to Optimise for Fan-Out Queries
To optimise for fan-out queries, build content around the full question cluster behind the search, not just the main keyword. Cover the likely sub-queries, use clear answer sections, support claims with evidence, and make the page easy for AI systems to parse.
Here is a practical process.
1. Start With the Main Prompt
Choose a real search prompt, not just a keyword.
Example:
best CRM automation setup for a small legal firm
Then ask what the user really needs to know.
2. Generate Likely Sub-Queries
Break the prompt into hidden questions:
- Which CRM fits legal firms?
- What automation is safe for client data?
- How should leads be routed?
- What reminders should be automated?
- What systems need to integrate?
- What should not be automated?
- How much setup is needed?
- What compliance issues matter?
These become your content sections.
3. Build a Page That Answers the Cluster
Your content should include:
- direct definition
- decision criteria
- comparison table
- use cases
- risks
- examples
- FAQs
- implementation steps
- common mistakes
- next action
This structure makes the page more useful for humans and machines.
4. Make Answers Extractable
AI systems need clean, quotable sections.
Use:
- short paragraphs
- clear H2s and H3s
- tables
- bullet lists
- concise definitions
- schema where useful
- descriptive page titles
- strong internal links
Do not hide the answer under a long introduction.
5. Add Original Information
Commodity answers are easy for AI to generate.
Add something harder to replace:
- client examples
- local context
- pricing logic
- screenshots
- workflows
- expert judgement
- benchmark patterns
- templates
- mistakes you have seen
This supports both GEO and SEO.
Common Mistakes
Mistake 1: Treating GEO as a Separate Channel
GEO is not separate from SEO.
Most GEO-friendly practices also help traditional search.
Clear structure, authority, internal links and useful answers help both.
Mistake 2: Targeting One Keyword Too Narrowly
A page that only repeats one keyword may miss the fan-out.
Instead, cover the surrounding questions.
Mistake 3: Writing Generic Answers
AI can already produce generic summaries.
Your content needs proof, examples or a stronger point of view.
Mistake 4: Ignoring Measurement
Traditional SEO tools will not show the full GEO picture.
You still need Google Search Console and rank tracking, but you may also need prompt testing and AI visibility checks.
A newer research survey notes that GEO measurement is still developing and that terminology, metrics and evidence standards remain uneven across the field.
Mistake 5: Forgetting Technical SEO
AI systems still need accessible content.
If your page cannot be crawled, indexed or clearly understood, it is less likely to help in SEO or GEO.
Best Practices
Best Practices for Fan-Out Queries
The best way to optimise for fan-out queries is to build content that answers the main query and the likely sub-queries behind it.
Use this checklist:
- Identify the full search intent.
- Map likely sub-questions.
- Build topic-complete pages.
- Use clear answer-first headings.
- Add examples and proof.
- Include comparison tables where useful.
- Strengthen internal links.
- Use reliable sources.
- Keep technical SEO clean.
- Track both rankings and AI visibility.
Expert Tip
Do not ask, “What keyword are we targeting?”
Ask, “What bundle of questions is the AI likely to ask on the user’s behalf?”
That one shift will improve your content brief immediately.
Quick Summary
Fan-out queries are the sub-queries AI search systems create from one user prompt.
They matter because AI search retrieves information across the full topic, not only the exact query text.
SEO and GEO are not enemies.
SEO helps pages rank and get discovered.
GEO helps content become useful inside generated answers.
The best strategy is to build clear, complete and trustworthy content that works for both.
Conclusion
Fan-out queries explain why AI search is changing content strategy.
A user may ask one question, but the AI system may search across many hidden sub-questions before producing an answer.
That means SEO can no longer rely on single-keyword pages with shallow coverage.
Pages need to answer the wider cluster of needs behind the query.
GEO adds another layer by asking whether your content can be retrieved, understood, cited and used inside generative answers.
But GEO does not replace SEO.
The two are converging.
Strong SEO gives content the technical and topical foundation to be discovered.
Strong GEO makes that content easier for AI systems to use in generated responses.
The businesses that win will not be the ones stuffing more keywords into pages.
They will be the ones building content that answers the full question better than anyone else.
Key Takeaways
- Fan-out queries break one user prompt into several hidden sub-queries.
- Google says AI Mode uses query fan-out across subtopics and data sources.
- Ranking for the literal keyword is no longer enough.
- Pages need to answer the wider cluster behind the query.
- GEO focuses on visibility inside generative AI answers.
- SEO focuses on rankings, clicks and organic search visibility.
- GEO does not replace SEO; the two are converging.
- Clear, structured, evidence-led content supports both SEO and GEO.
FAQs
1. What are fan-out queries?
Fan-out queries are the sub-queries an AI search system creates from one user prompt. The system searches across those subtopics and combines the results into one generated answer.
2. What is query fan-out in AI search?
Query fan-out is a retrieval method where AI search breaks a complex question into multiple related searches. Google says AI Mode uses this technique to search across subtopics and data sources.
3. How do fan-out queries affect SEO?
Fan-out queries mean SEO content must cover the full topic cluster behind a search, not only the exact keyword. Pages that answer related sub-questions clearly may become more useful to AI search systems.
4. What is GEO in marketing?
GEO stands for Generative Engine Optimisation. It focuses on improving how content appears, gets cited or is used inside AI-generated answers from tools like AI Overviews, ChatGPT, Perplexity and Gemini.
5. What is the difference between SEO and GEO?
SEO aims to rank URLs in search results. GEO aims to make content retrievable, quotable and cite-worthy inside generative AI answers. They overlap because both need clear, useful and trustworthy content.
6. Does GEO replace SEO?
No. GEO does not replace SEO. Most GEO-friendly practices, such as clear structure, topical depth, authoritative sourcing and direct answers, also improve traditional SEO.
7. How do I optimise for fan-out queries?
Map the likely sub-questions behind the main query. Then build content that answers each one with clear headings, examples, tables, FAQs, source-backed claims and strong internal links.
8. How do you measure GEO?
GEO measurement is still developing. Common methods include prompt testing, AI citation tracking, brand mention monitoring, AI visibility tools and comparing AI answer presence with traditional SEO metrics.
