Claude and Gemini can turn one complex research question into a sequence of smaller searches rather than relying on a single query. Both can use information discovered during research to decide what to investigate next.
The important AEO implication is that your content is competing for many hidden retrieval queries generated by the AI system, not only the sentence the customer originally typed.
What does it mean to break one question into multiple searches?
Complex questions usually contain several information needs.
Consider:
"Which AI visibility consultant is best for a small B2B software company with a limited marketing budget?"
Answering that well may require research around:
- AI visibility consulting
- B2B marketing experience
- Small business suitability
- Pricing
- Service scope
- Customer reviews
- Industry expertise
- Alternative providers
An AI research system can identify these subproblems and search for them separately.
This process is sometimes described as query decomposition, query expansion, iterative research, or query fan out depending on the system and implementation.
How does Claude research a complex question?
Anthropic describes Claude Research as an agentic process that conducts multiple searches which build on one another.
Claude does not need to know every useful search before starting.
It can retrieve an initial set of information, evaluate what it learned, identify missing information, and then perform additional searches. Anthropic says Research explores different angles and works through questions systematically.
Anthropic explains this process in its Claude Research documentation.
This creates a progressive search pattern:
- Start with the research question
- Search an initial topic
- Evaluate what was found
- Identify gaps or new leads
- Search again
- Compare additional evidence
- Continue until enough information is available
- Synthesize the findings
The later searches can therefore depend on what Claude discovered during earlier searches.
How does Gemini research a complex question?
Gemini Deep Research also uses a multistep process.
Google says Gemini creates a research plan before beginning a Deep Research report. The user can review or edit that plan before research starts.
Google Search is included as a research source by default, although other connected sources can also be selected.
Google's current Gemini Deep Research instructions describe the research plan and source selection process.
Google has also described Deep Research as repeatedly searching, finding useful information, and starting new searches based on what it learns.
That makes Gemini's research process iterative rather than a one time search request.
How is iterative research different from query fan out?
The concepts overlap, but they are useful to distinguish.
Query fan out generally means generating several related searches around different parts of a question.
Iterative research means later searches can be influenced by information found during earlier searches.
A research agent can use both.
For example, the original question might generate parallel searches around pricing, customer type, and service capabilities.
One of those searches might reveal an unfamiliar competitor or category term. The system can then start another search specifically around that new information.
Google Search officially says its AI features can use query fan out by issuing multiple related searches across subtopics and data sources.
Claude Research similarly conducts multiple searches that build on previous findings.
The result is a search tree rather than one keyword.
Why can Claude and Gemini discover different brands?
Each system can break the same question into different subproblems.
Suppose someone asks:
"What is the best marketing agency for a founder who wants more visibility in ChatGPT and Gemini?"
Claude might explore concepts such as:
- AI visibility consulting
- AEO agency services
- ChatGPT optimization
- Founder marketing consultants
Gemini might generate searches around:
- AI search marketing agencies
- Generative engine optimization
- Gemini visibility consulting
- SEO agencies offering AEO
- AI marketing for small businesses
Those query families can expose each system to different competitors and sources.
This is one reason two strong research assistants can produce reasonable answers that recommend different companies.
What does this mean for AEO content strategy?
AEO content should cover the information surrounding a customer problem, not repeat one target keyword.
Start with the main buyer question and map the supporting questions an AI system may need to answer before it can make a recommendation.
For a service business, those could include:
- Who is the service for?
- What problem does it solve?
- What is included?
- How is it different from alternatives?
- What does it cost?
- What evidence supports the claims?
- What industries does the company serve?
- What limitations should buyers know?
- How does implementation work?
- When should someone choose a competitor instead?
These supporting questions create additional retrieval opportunities.
The goal is coherent topic coverage, not producing a separate thin page for every imagined AI prompt.
How should you structure content for multiple AI searches?
Build content around related information needs.
A strong service page can answer the core commercial question while supporting articles investigate narrower topics in depth.
Useful content types include:
- Service pages
- Comparison articles
- Pricing explanations
- Case studies
- Original research
- Frequently asked questions
- Industry specific guides
- Implementation guides
- Alternative pages
- Problem focused educational content
Then connect those resources with clear internal links.
This gives both human visitors and search systems a more complete picture of your expertise.
Our AI visibility advisory uses customer questions and likely retrieval paths to prioritize content rather than producing pages simply because an AI keyword tool suggested them.