Query fan-out is a key concept in the evolution of Google’s artificial intelligence (AI) models, especially in the context of AI Mode or AI Overviews. To understand it properly, it is essential to go beyond the traditional search based on exact keywords and add an extra layer to the concept of relevance.
What is Query Fan-Out?
Query fan-out is a process by which Google breaks down a user’s complex query into multiple smaller, related semantic subqueries. These subqueries run in parallel to gather information from different perspectives, allowing the AI to generate a more complete, contextual, and accurate response.
The most accurate translation into Spanish would be ramification of queries, which gives a more precise idea of what was intended to be achieved and how to achieve it.
Where does this procedure for understanding and enriching queries come from?
We have witnessed the launch – quite opaque due to EU legislative issues – of Google AI Mode, more advanced than Google AI Overviews (Google AIO): it uses query fan-out to emit multiple simultaneous searches and merge the results, offering complete and multimodal answers to complex questions, surpassing the capabilities of AI Overviews.
For example, if a user searches for “how to choose an SEO consultant,” the AI is not only limited to that specific query, but also searches for:
- “Advantages of freelance SEO consultants”
- “SEO consultants with more experience”
- “Average price of a freelance SEO consultant”
- “Experience of SEO consultants for the industrial sector”
- “SEO consultants with the ability to communicate and work in English”
All of these subqueries are performed internally, and the end result is a synthesized, coherent answer that addresses all of these points without the user having to do multiple separate searches.
The goal is to anticipate the user’s needs and offer them the most relevant information in one go.
This is good, but it has serious implications for content creators, whether individuals or businesses.
Implications of Query Fan-Out on SEO
This change in the way Google processes searches has significant implications for SEO strategies:
End of Isolated Keywords
It’s no longer enough to optimize for a specific keyword. The focus should shift to the search intent and the full semantic field. Your content should comprehensively cover a topic, anticipating potential questions and subtopics that a user might have.
Modular and Deep Content
To be “suitable” for query fan-out, your content must be structured and easy to digest by AI. This means that it should have a good heading structure (H1, H2, H3), clear and concise paragraphs, and a focus on answering concrete questions. AI will be able to extract specific parts of your content to answer a particular subquery.
Importance of Structure and Authority (E-E-A-T)
AI looks for reliable sources to synthesize its answers. Therefore, your website’s experience, authority, and trust (E-E-A-T) become more important than ever. Content that demonstrates in-depth knowledge about the topic will be more likely to be cited in an AI response.
Thematic Clusters
This technique reinforces the need to create content hubs or thematic clusters. Instead of creating isolated articles, it is about generating a central content (pillar) on a broad topic and linking it to other more specific articles that delve into the related subqueries. This shows Google that you’re an authority on the subject.
Appearance of the Zero Click Effect
As I said before, the branching of results can be of great help to the user, but at the cost of taking him away from the results in list format, where the user’s intention and his own criteria could lead him to visit one or another result.
By generating a more complete and diverse response around the main topic of the query, the user may not feel the need to expand on that answer, or to expand it by references other than the organic list of results.
This enhances the critical importance of 2 concepts:
- The main topic of the consultation to which I referred is neither more nor less than an Entity. In other words, the branching or diversification of the original query takes us directly to the core of the Semantic Web.
- The Zero Click or in Anglo-Saxon jargon, Zero Click. This effect has been causing a great distortion in the practice of SEO. More specifically, it should say “Google-oriented SEO”, but as is obvious in the Western market, it is still understood that the main objective is to position oneself and have visibility in the Google latifundia. This is already in the difficult terrain of the future of SEO, but it is clear that this future will continue to have the first concept – Entities – and their Relationships.
Structured Data and Advanced SEO Techniques for Generative Search
Implementing Structured Data is indispensable to help search engines and LLMs better understand the context and relationship between different pieces of content on your website. It must be taken to the level of Entities.
In addition, the application of advanced LLM techniques, such as the use of DSH (Homogeneous Semantic Descriptor), can significantly improve AI’s ability to interpret and organize information effectively.
The DSH allows a coherent and unified representation of semantic data, facilitating better understanding and processing by search algorithms. Internal search engine procedures and LLM generation reasoning such as those described in Google’s documentation on Query Fan-Out and RAG, are supported by a DSH deployment that provides greater depth and context. In several cases with DSH customers, regardless of size or sector, the behavior is the same, especially pronounced after the latest Google update.
Retrieval-Augmented Generation (RAG)
The RAG describes the process of generative responses to search queries, which are augmented by collecting data relevant to the query that is not found in the LLM’s previous learning background, but in the current and live indexes of search engines. This means that an AI assistant can answer your search query with background information, such as evergreen data, and also go to Google search results to improve the answer.
What is RAG?
Retrieval-Augmented Generation (RAG) is an architecture that combines the retrieval of relevant documents from a knowledge base and the generation of responses using a language model (LLM), based on the retrieved documents. This approach improves the accuracy and factual support of the responses generated.
Relationship with Query Fan-Out
Query Fan-Out is a strategy within the RAG workflow, especially useful in complex scenarios:
- Query rewrite: The LLM can generate multiple versions of the query.
- Document Retrieval: Each version can activate different search engines.
- Re-ranking: The results retrieved are ordered according to relevance.
- Response Generation: The LLM synthesizes the information retrieved.
This approach improves the quality and depth of responses, especially in systems such as the Agentic RAG, which use agents to self-assess and refine responses.
SEO in the Age of AI
- It’s no longer just about ranking, but about being the source of information that Google will use to generate its own responses, ensuring that your content is optimized for both current algorithms and future innovations in semantic search.
- Traditional SEO is still in force, but the ranking factors have changed.
- It is important to understand the implications of Query Fan-Out, but let’s not lose sight of fragment indexing, known as passage indexing.
Passage Indexing
Passage Indexing divides long documents into smaller chunks, each of which is indexed individually. This allows specific parts of text to be retrieved instead of entire documents, improving semantic accuracy.
Relationship with RAG and Query Fan-Out
Passage Indexing appears in the indexing phase of the RAG workflow, allowing you to find relevant specific snippets. Query Fan-Out amplifies the user’s query in multiple variants, improving semantic coverage and the diversity of information retrieved.
SEO is still SEO, but much less tactical than strategic
In short, the fan-out query is Google’s internal mechanism for processing complex queries in the age of AI. To adapt your SEO strategy, you need to stop thinking about keywords and start creating deep, well-structured content that comprehensively answers all of a user’s possible intentions and sub-questions about a topic.
Implementing Structured Data and advanced LLM techniques, such as DSH, along with understanding RAG and Passage Indexing, are essential to maintaining relevance and authority in today’s SEO landscape.
Ricard Menor, SEO Manager SOLID SEO Management Services
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© Ricard Menor, SOLID SEO Manager