Query Fan‑Out: AI Search Explained | Optimize Content Strategy

What is Query Fan-Out?

A query fan-out is a technique used by AI to understand the user’s search intent, context, or other related entities and provide relevant answers accordingly. The technique involves breaking down the searched query or query decomposition into related subqueries, followed by making a Google search on each of them.

It results in receiving the documents, blogs, and other content specific to each subquery, which is combined to offer a comprehensive and detailed answer concerning the specific query. Note that each subquery is searched simultaneously to provide a quick response.

The query fan-out technique finds applications in the currently used Google AI mode and also in the AI overviews. However, simple queries that require direct answers do not use the fan-out technique.

What Does the Patent Describe?

Google filed the patent named ‘Thematic Search’ in December 2024, which sheds light on Google's AI results delivery method. The patent is similar to the query fan-out technique and describes a search system designed for broad or complex questions. The novel approach with this technique relies on traditional search results as a foundation, followed by the use of AI to generate a contextual summary from the obtained results.

The technique involves query decomposition, followed by categorizing the results into themes. These are independently searched on the web. The obtained information is summarized to generate a concise, theme-specific summary tailored to the user's query, helping them gain a clear view of the answers. It eliminates the requirement to click multiple links and scroll through a hefty amount of information.

Did You Know?🔍

Most AI-powered queries expand into 2–5 sub-queries, with approximately 84% of fan-out sub-queries being closely related "neighbors" that share URLs with the original search. (Source: Surfer SEO)

What is a Thematic Search Engine?

The thematic search engine works by understanding the user search query in terms of context and intent. It does so by identifying related search queries and categorizing them by theme. The search is conducted based on the themes, and AI generates a summary for each. It further combines them to provide information regarding the query, along with related and relevant details. The query analysis and summary development capability utilizes Natural Language Processing.

How Does Query Fan-Out Work?

Let’s understand the working of Google query fan-out in a stepwise manner:

Step 1: Query Submission

The user types in their search query on Google’s AI-powered search interface.

Step 2: Query Analysis

Google analyzes the query using advanced NLP to determine the following aspects:

• User intent
• Complexity of the question
• Type of response needed

Step 3: Determine the Requirement of Fan-Out Technique 

Simple queries do not require fan-out execution, while complex queries do. Hence, if the user has searched for the ‘founder of Apple’ or a question like ‘11 + 3’, then the fan-out technique will not be triggered. However, if the query is ‘how to use AI for research’, the fan-out processing will be triggered.

Step 4: Sub-Query Generation

The system now breaks down the complex query into related and multiple subqueries, performing query decomposition. These queries are primarily based on the original search query's intent. It is also dependent on the possible follow-up questions, semantic understanding, logical information architecture, and user behaviour patterns. These subqueries, also referred to as meaningful chunks, are of the following types:

• Fixed-size chunking to align content with model limits
• Semantic chunking for grouping the related sentences
• Recursive chunking for splitting unstructured content
• Layout-aware chunking, where the content is divided as per the HTML structure, is the default in Vertex AI Search through LayoutBasedChunkingConfig

Step 5: Sub-Query Processing

The generated subqueries are now independently processed in parallel. It involves searching across a variety of sources, including web search indexes, vertical engines, Google’s Knowledge Graph, large language models, AI systems, and specialized databases.

Step 6: Result Analysis and Answer Synthesis

The results obtained for all sub-queries are evaluated based on quality signals and Google’s rankings. Results from various sources are combined to generate context-rich responses. It is accomplished through natural language processing and is designed to directly address the original query, incorporating relevant supporting details.

Step 7: Final Answer

The user-specific, well-structured theme-based answer is provided using the query fan-out technique. It eliminates the need to browse multiple pages while supporting understanding of the complex topics.

How Does Query Fan-Out Impact Search Ranking?

The query fan-out technique has indeed impacted the search ranking. While traditionally, developing content based on keywords can help improve content ranking. However, with AI’s increased focus on context, intent, and follow-up questions, the content’s value delivery now has a significant impact on search rankings.

AI divides the single query into multiple related questions. Therefore, content covering a wide and detailed aspect of the topic is likely to be included in the search results. On the other hand, sticking to the specifics may lead to missing out.

Furthermore, the concept of passage indexing also plays a role in this context. It means the section of the content may rank independently in comparison to the complete page or content.

Comparison of Query Fan-Out and Thematic Search

How Google AI Mode Handles and Responds to Queries?

When a query is put into Google AI mode, it follows the mentioned Google query fan-out mechanism to provide an answer:

• Expands the query into multiple subqueries, reflecting the user's intent and providing further relevant information. It does so in the following manner:

1. Based on aspects like features, specifications, ingredients, formats, and use cases, which reflect actionable insights about the query
2. Based on parameters such as durability, cost, brand comparisons, ease of use, and verified reviews, which reflect important aspects of the query
3. Based on time of day, age group, location, experience level, and search history, which reflects uniqueness for the specific user

• Generated subqueries are searched to find an answer from the available resources
• The multiple obtained results are merged and summarised to offer simple information

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Thematic Search: Implications for Content and SEO

The improved AI-based search has two implications for content and SEO. These are either high or minimal click-through rates. Here is why:

• Users looking to shortlist web pages based on content relevance and quality will directly head to the top-ranked sources of AI overview. This action is expected to contribute to high click-through rates
• The alternative possibility is that users simply read the AI summaries, thus not visiting the page themself for detailed information. This will contribute to zero-click behaviour

Techniques and Tools That Will Help You Find a Fan-Out Query

To rank high concerning any topic or word, finding a fan-out query is essential. Identifying this requires focus on the perspective of both the user and the AI.

1. User Perspective

This part is relevant because AI evaluates what the user specifically wants. Hence, if your developed content also addresses the user’s needs, you are on the same page with AI.

To incorporate this, if you are writing about a new topic on which you have no prior knowledge, the best approach would be to gather basic information or introduce yourself to it. Follow it with listing down the questions that come to your mind. If you are familiar with a topic, you need to list the basic details and develop the content around it. Ensure that the content directly answers the user's query and fan-out query.

2. AI Model Perspective  

To understand how AI addresses a specific query or keyword, use AI itself. Make a Google search and check the answers to the AI overview. Now, analyze the points covered in the generated answer.

Determine whether it addresses the applications, categorizes the topic, explains its mechanisms (if it's a concept), includes the pros and cons, or any other relevant details. Now you have direct information on how AI performs when a similar query is searched, and hence you can generate the content accordingly.

3. Tools

Besides understanding the perspective, you can also utilize relevant tools for assistance. Here are relevant insights into the same:

• Answer the People: This tool will provide all the questions people have asked concerning the topic. You have to type in the keyword to gain the insights
• Keyword Insights: This is an AI-based tool that provides live data from Reddit, People Also Ask, Google Autocomplete, and other sources
• InLinks: It is an entity-based semantic SEO tool that helps with on-site optimization and data for on-page content creation
• People Also Ask: Search your keyword on Google and scroll down to find this section. It provides the most related queries that Google considers relevant to the user
• Related Searches: This section, located at the bottom of the Google page, also provides insights into relevant queries related to the topic
• Google Trends: It can help you learn about the rising queries for a topic and identify search patterns related to it

How to Optimize Content Strategy?

To enhance the content and optimize the SEO strategy to increase clicks from the users, the key aspects to focus on are as follows:

• Ensure that you incorporate complete information rather than focusing on surface-level details. It involves writing the content to answer the intent of the keyword and users' queries regarding the topic
• Include related terms to the keyword to gain high discoverability, as it forms the base of this technique
• Create content that aligns with Google's content quality and credibility evaluation criteria. The content following the Expertise, Experience, Authoritativeness, and Trustworthiness (EEAT) framework is prioritized for generating responses
• The different sections and paragraphs must answer specific questions. Independent designing of the sections can contribute to listing at the top through passage indexing
• Incorporate unique insights and develop content through high-quality research
• Focussing on improving the formatting and clear structure to enhance the quality of content
• Emphasize the conversational and adaptive nature of the content rather than sticking to simply one aspect

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Conclusion

Query fan-out is an effective technique that contributes to the intelligence of AI. It helps generate responses that satisfy users by answering what's required and anticipating any questions that may arise next. It works by decomposing the user queries into related subqueries that comprehensively address the intent and context. The search results obtained from the mentioned subquery search are summarized through natural language processing to provide an information-rich answer.

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FAQs

1. Which Google products use Query Fan‑Out?

Google's query fan-out technique is used in providing answers for AI overview, AI mode, and deep search.

2. What sources are used during fan‑out?

The different sources referred to during fan-out are Google Finance, Shopping Graph, Knowledge Graph, and the broader web index.

3. How many sub‑queries are generated?

The number of generated subqueries depends on the original query. Hence, the value is not fixed.

4. How are themes or sub‑queries identified?

The themes or subqueries are identified based on the user's query intent, context, and potential follow-up questions.

5. Does ChatGPT also use fan‑out?

Yes, when the search query requires making a web search for answering the question, then ChatGPT also uses the fan-out technique. 

6. How can creators optimize for fan‑out?

To optimize content for fan-out, the key requirement is to develop comprehensive content that encompasses a wide range of aspects related to the topic. The content should include the related terms and offer answers to possible follow-up questions as well.

7. How can users replicate fan‑out behavior?

Users can replicate fan-out behavior by evaluating Google's AI search results to identify the structure it follows for a given topic. They can develop the content accordingly. Further, including comprehensive details about the topic is also an effective measure to replicate the fan-out behaviour.