{"id":2596,"date":"2026-02-04T16:20:54","date_gmt":"2026-02-04T16:20:54","guid":{"rendered":"https:\/\/grp-digital.com\/nl\/kennisbank\/ai-zichtbaarheid\/query-fan-out\/"},"modified":"2026-09-16T08:48:27","modified_gmt":"2026-09-16T08:48:27","slug":"query-fan-out","status":"publish","type":"post","link":"https:\/\/grp-digital.com\/en\/kennisbank\/ai-zichtbaarheid\/query-fan-out\/","title":{"rendered":"Query fan-out: why ranking for one keyword isn\u2019t enough anymore"},"content":{"rendered":"

You type \u201cred phone case\u201d into an AI search engine. Behind the scenes that one question is split into ten others: which model, which material, does it discolour, which shop is trustworthy. That\u2019s called query fan-out, and it changes how you get found. Ranking for one keyword is no longer enough.<\/p>\n\n

\"Illustratie:
Illustration: An LLM splits one search query into multiple sub-queries and searches in parallel.<\/figcaption><\/figure>\n\n\n

What is query fan-out?<\/h2>\n

Query fan-out is the process by which an AI search engine automatically expands one query into multiple sub-queries, to give a more complete answer. One query is thus expanded into many, to give the model enough context. Where traditional search engines worked one-to-one (one query, one result list), fan-out flips that: one question triggers dozens of searches at once.<\/p>\n

How it works, in six steps<\/h2>\n
  1. Analysis:<\/strong> the LLM determines the intent and complexity of your question in milliseconds.<\/li>
  2. Decomposition:<\/strong> the question is split into sub-questions from different angles.<\/li>
  3. Parallel retrieval:<\/strong> all sub-questions search simultaneously in web indexes, knowledge graphs and databases.<\/li>
  4. Synthesis:<\/strong> the results are combined, with sources that appear in multiple lists counting more heavily.<\/li>
  5. Scoring:<\/strong> each document gets a relevance score based on its position across those lists.<\/li>
  6. Re-ranking:<\/strong> everything is reordered by total score into one answer.<\/li><\/ol>\n

    Many of those searches are never typed by a person<\/h2>\n

    This is where it gets interesting. The LLM invents these sub-questions itself, often about topics the user never explicitly mentioned. So they\u2019re not keywords people search for; you can\u2019t target them directly with a keyword list. The LLM can fire dozens of those searches at once on a single question, and also infers context from behaviour and history, not just your literal search terms. Google uses this fan-out technical SEO in its AI Mode.<\/p>\n

    Which sub-questions does an LLM ask?<\/h2>\n