What is AI query fanout?
AI query fanout is the expansion of one topic into several narrower searches. A provider may use those searches to cover different parts of the task before producing an answer.
Start here if the term is new or if you need to explain it without claiming access to hidden model internals.
One broad topic can contain several different jobs
Take “best SEO tools”. One person may want a shortlist, another needs current prices, and an agency may care about audits, reporting or client access. A short starting phrase hides those different jobs. A web-enabled AI system can split the topic into narrower searches before answering.
The exact branches depend on provider, model, settings and time. A run might cover a general comparison, named products, pricing and a particular feature. Another run can make a different choice without either list becoming a complete map of the topic.
Search queries are intermediate actions used to find documents. A query does not prove that the provider accepted every result, and a cited page does not reveal how much it influenced the final answer.
One search action can contain several query strings. ai-fanout.com therefore reports search actions, exposed queries and sources separately instead of inventing a one-to-one relationship.
Some provider APIs return search-action objects, query strings and source annotations. When a query string is present, this site can report it as an observation from that named run.
The site does not inspect ChatGPT or Gemini consumer interfaces, browser network traffic, system prompts, private retrieval traces or chain of thought. An API observation is useful evidence, but not a capture of every internal step.
What the result is useful for
The list can reveal wording, user jobs and source needs that were easy to miss. Use it to review an existing page, plan research and decide what deserves closer investigation.
It is not search-volume data, a stable keyword set or a publishing plan. Several queries may belong on one strong page, while another branch may have no measurable demand or relevance.
Record the topic, provider and exact model, language, country, time, tool version and method version. Without them, two screenshots cannot be compared responsibly.
- Topic and locale
- Provider and model
- Date and time
- Tool and method version
Sources used
These sources support the functions and limits described here. They do not prove claims beyond that scope.
- AI features and your websiteOpen source
Google describes query fan-out publicly without exposing a general private-query inspection interface.
- OpenAI web search tool guideOpen source
Documents web_search_call output and search actions that can include the query or queries searched. It does not expose chain of thought or guarantee that every route returns query strings.
- Gemini grounding with Google SearchOpen source
Documents Gemini 3.7 Flash Google Search, google_search_call arguments.queries, cited URL annotations and per-query billing.