Fan-outs
What is fan-outs in seo?
Its a technique where ai system takes the user’s query and splits it into multiple sub queries to be more specific about it according to the user’s search relevancy or recent similar searches to gather a personalized comprehensive answer.
Instead of looking for one page with the exact keyword, the ai acts as a personal research agent, running multiple micro searches simultaneously to cover every angle of the user’s request.
How it works?
The AI breaks your prompt into sub-topics. For example, if you search “Best laptop for video editing under rs.50,000,” the fan-out might include:
- “Top rated video editing laptops 2026”
- “Laptops with 32GB RAM under 50k”
- “Best GPU for 4K editing in budget laptops”
The system runs all these sub-queries at once across the web, the Knowledge Graph, and specialized databases
It gathers the “best” fragments from different websites and merges them into one comprehensive AI Overview, by the sources it used for each part.
Why it matters in seo?
Ranking #1 for a specific keyword is no longer usefull. If your page doesn’t answer the sub-queries the AI generates, you won’t be shown in the AI summary.
In AI Mode, users often get their answer without clicking a link. Being the source of a “citation” within the AI response is the new goal.
Authority is Key. To be picked up by a fan-out, your site needs to cover an entire topic cluster deeply. AI models prefer sources that demonstrate “EEAT” (Experience, Expertise, Authoritativeness and Trustworthiness) across multiple related sub-topics.
Research shows that 95% of fan-out queries have 0 monthly search volume. This means the AI is searching for highly specific, longtail information that traditional keyword tools mostly ignore.
Uses of fanouts:
For search engines: Informational retrieval
The primary use of fan-outs is to move from keyword matching to reasoning.
- Decomposing complex intent: if you ask “is it safe to hike in pushpagiri alps in october?”, the ai fans out to check weather patterns, trail, closures, gear requirements, and sunset times. It uses these sub-queries to build a complete answer from 10+ sources at once.
- Grounding the fact-checking: by fanning out a claim into multiple verification queries across official sites and reviews, the ai reduces “hallucinations” and ensures the answer is grounded in reality.
- Predicting follow-ups: fan-out is used to anticipate what you will ask next, if you search for “business bank account”, the ai proactively fans out to find “monthly fees”, “ATM access”, and “sign up bonuses” to give you the data before you even ask for it.
Chunks
What is chunks in seo?
An seo chunk is a self-contained, semantically coherent block of information within a larger piece of content.
Instead of seeing your article as one long 2000 word stream, ai search engines like google gemini or search “AI mode” see it as a collection of 5-10 independent “knowledge units”. A chunk typically ranges from 150 to 300 words and focuses on answering one specific sub-topic or question perfectly
How it works?
Chunks is a bridge between your writing and an AI’s brain[database].
- Decomposition: when you publisha page, the search engine doesn’t index the URL. it breaks the text into segments.
- Vectorization: each segment is turned into a vector.
- Semantic matching: when a user asks a question, the ai doesn’t look for your page title. It looks for the specific chunk whose vector most closely matches the user’s intent.
- Extraction: the AI plucks your chunk out of your article and inserts it into an AI overview or a chat response, often citing you as the source.
Why it matters in seo?
In the era of zero click searches and AI overviews, chunking determines whether you are visible or invisible.
- Precision over volume: AI models have limited “context windows”. They cannot ingest your entire 5000 words guide to answer a simple question. The need a 200 word chunk that gets straight into the point.
- Ranking for Fan-outs: As i said, AI fans out complex queries into sub questions. If your content isn’t chunked, the AI won’t find the specific fragment. It needs to answer those sub queries.
- Reduced dilution: if you mix three different topic in one long section, the meaning becomes blurry[less visible to ai]. The ai will skip your blurry content in favour of a competitor’s sharp and single topic chunk.
Uses of chunks:
- Scannability: allows users to find the answer immediately without any contradictions.
- Citations: provides clean, quotable snippets for ai overviews and voice search.
- Featured snippets: increases the hit rate for position zero results by providing direct answers.
seo Fan-outs vs chunks
| Feature | Query Fan-outs (Search Engine Action) | SEO Chunks (Content Strategy) |
|---|---|---|
| Definition | The AI "exploding" one prompt into multiple sub-searches to find a complete answer. | Breaking a long article into standalone, "extractable" units of information. |
| Primary Goal | To cover all angles of a user's complex intent. | To be the specific "data block" that the AI selects to answer a sub-query. |
| Example Topic | "Planning a budget trip to Calicut." | "Cost of a Rail Pass." |
| Example Content | The AI generates sub-queries for: "Current INR to USD exchange rate," "Average hostel price in Calicut," and "Cheap food spots in Calicut." | A table on your site titled "2026 Kerala Rail Pass Pricing" with a clear 1-sentence summary of who should buy it. |
| Success Metric | High Contextual Coverage: The AI successfully answered every part of the prompt. | High Citation Rate: Your specific section was quoted by the AI as the source of truth. |
| Visual Analogy | Like a Tree: One trunk (the query) growing many branches (sub-queries). | Like a Lego Set: Individual blocks that can be pulled out and used to build something new. |