A single AI prompt can trigger 9 to 11 underlying web searches, and ChatGPT's deep research mode once ran 420 searches for one question about a phone case. That one-to-many expansion, called query fan-out, is central to how AI search engines work and why topic coverage beats single-keyword optimization.
How AI Search Engines Work: The Two Sources Behind Every Answer
AI search engines work from two distinct information sources: static training data and real-time retrieval through web search APIs. Training data is the model's memorized snapshot of the internet, while retrieval, built on retrieval-augmented generation (RAG), lets tools like ChatGPT and Google's AI Mode fetch fresh pages the moment a question needs them.
This distinction is the first lesson in Ahrefs' AEO course, presented by Sam Oh in April 2026, and it matters because each source responds to a different kind of optimization. You cannot influence training data directly in the short term, but you can absolutely influence what a live web search returns.
The practical takeaway is reassuring for anyone with SEO experience. Ranking in Google, earning backlinks, and publishing quality content directly influence whether AI picks up your pages during real-time retrieval. The skills transfer; only the output format changes from a ranked list to a synthesized answer.
Training Data vs Real-Time Retrieval (RAG)
Training data is the massive corpus a model learned from: books, websites, PDFs, social media, and YouTube transcripts. Ask ChatGPT who the CEO of Apple is and it answers Tim Cook instantly, without searching, because the pattern is baked in. The weakness is that this snapshot is static and updated roughly every six months, so a product launched last week does not exist in it yet.
Real-time retrieval fills that gap. When a question is too specific or too recent for training data, the system searches the web through APIs, pulls back relevant pages, reads them, and generates a response grounded in what it found. Patrick Stox, speaking in the Ahrefs course, describes it as two parallel processes: the trained LLM data, and a secondary process that fetches relevant pages which then carry their own probabilities into the answer.
That means there are two ways to influence what AI says about your brand. The first is to be mentioned so widely across the web that your brand enters the training data itself. The second is to make sure your content surfaces when the AI searches the web in real time, which is where conventional SEO work pays off.
How an AI Answer Is Generated, Step by Step
The lesson breaks the journey from prompt to answer into a repeatable pipeline. Knowing the order helps you see where your content can enter the process:
- The user types one prompt into an AI search engine or assistant.
- The system expands that prompt into many long-tail sub-queries through query fan-out, and runs them in parallel.
- The engine merges and scores results from those searches, drawing on both training data and freshly retrieved pages.
- It generates a single response, citing or mentioning the sources that best satisfied the sub-queries.
- Because a temperature setting adds deliberate randomness, step 4 produces a different answer mix each run, which is why citations shift between brands over time.
Query Fan-Out: Why One Prompt Becomes Dozens of Searches
Query fan-out is the technique where a single AI prompt is expanded into many long-tail sub-queries that run simultaneously. Traditional search moved from one-to-one (one query, one results page) to many-to-one (different phrasings returning the same results). AI search flips the model to one-to-many.
The scale is larger than most people expect. Research from Seer Interactive and Ahrefs, cited in the course, found that the average prompt triggers 9 to 11 fan-out queries, with some reaching 28. In one example, ChatGPT's deep research mode ran 420 searches for a single question about buying a red phone case.
For a prompt like "plan me a 5-day trip to Japan in November", the sub-queries might include Tokyo neighborhood recommendations, November weather in Kyoto, and whether the Japan Rail Pass is worth it. The AI then merges information from across those searches into one answer.
The strategic consequence: relevance across an entire topic beats a page optimized for one keyword. A guide on starting a podcast that skips equipment, hosting, and promotion loses to a page that covers all three, because the AI will retrieve whichever source satisfies more of the sub-queries.
Can You See Fan-Out Queries? A Caution About Keyword Lists
Yes. The Brand Radar AI Responses report from Ahrefs shows fan-out queries behind ChatGPT and Perplexity prompts. For anyone studying how AI search engines work, this is a rare look at the intermediate layer that users never see.
But treat it as a lens, not a keyword list. Ahrefs' own guidance on query fan-out notes these queries are synthetic, generated by the AI in the moment, and inconsistent: the same prompt can trigger different fan-outs every time. Over 95% of them have zero search volume because no human would ever type them.
The right use is directional. If fan-out repeatedly surfaces equipment, hosting, and promotion for podcast questions, those are the topic areas your content must cover. Optimizing individual fan-out phrasings the way you would optimize long-tail keywords is a dead end.
How AI Decides Whom to Cite: Probabilities, Not Positions
AI citations are probabilistic rather than fixed. In traditional search, ranking third for a keyword today means ranking near third tomorrow. Ask an AI the same question five times and you might be cited three times, a competitor twice, and a third brand once, because generation involves a temperature setting that introduces deliberate randomness.
Stox's framing in the course captures it: AI outputs are built on probabilities on top of probabilities. Training data creates patterns, retrieved pages add their own signals, and the temperature setting ensures the model does not produce an identical answer every run.
This is why practitioners talk about AI visibility instead of AI rankings. There is no leaderboard to climb. The realistic goal is raising the probability of inclusion across many runs, which shifts measurement from position tracking toward share of citations over time.
Three Patterns That Raise Your Citation Probability
Ahrefs' data, as presented in the course, points to three patterns that move the needle. None of them replace SEO; all of them build on it.
- Consensus. When multiple sources across the web say the same thing about your brand, the AI is more likely to repeat it. Consistent, repeated mentions raise the probability the model picks your brand up.
- Freshness. AI-cited content tends to be about 25% fresher than what appears in a traditional SERP, because the AI actively favors recent information, especially on fast-changing topics.
- Authority. Pages that already rank well in traditional search have a head start. Ahrefs reports that 76% of AI Overview citations come from pages already in Google's top 10.
Do All AI Platforms Behave the Same Way?
No, and treating them as interchangeable is a common mistake. Since AI Overviews rolled out in Google Search in 2024 and AI Mode followed, the platforms have diverged in how they retrieve pages, how much they lean on Google's index, and how stable their citations are. The Ahrefs course makes this comparison the subject of its next lesson, but the broad picture looks like this:
| Platform | Main retrieval basis | Citation behavior |
|---|---|---|
| Google AI Overviews | Google's own index and rankings | 76% of citations come from pages in Google's top 10 |
| ChatGPT | Training data plus live web search via APIs | Lower overlap with Google results than AI Overviews |
| Perplexity web search with visible citations | Shows sources directly under each answer | |
| Google AI Mode | Google's index with heavier fan-out | Generates synthesized answers before organic listings |
For a strategist, the practical reading is that a page can be cited in AI Overviews yet absent from ChatGPT answers for the same question. Coverage has to be earned platform by platform, even though the underlying levers (consensus, freshness, authority) are shared.
Why Non-Dominant Brands Still Win in AI Search
The authority effect is strong but not total. Ahrefs' data shows 14% of pages cited in AI Overviews do not rank in Google's top 100 at all, and for ChatGPT the overlap with Google's results is even lower. That is a genuine opening for brands that are not dominant in traditional search.
The honest summary of how AI search engines work for SEO and AEO: SEO is the foundation, but the ceiling is higher for content that covers whole topics, earns consistent brand mentions, and stays fresh. Consensus drives probability, topic coverage matches fan-out, and existing rankings supply the authority head start.
One caveat worth stating plainly: these figures come from Ahrefs' own studies of AI Overviews and its customers' data, and platform behavior differs. Ahrefs itself notes that AI Overviews, ChatGPT, Perplexity Google's AI Mode do not work the same way, which is the subject of the course's next lesson.
FAQ: How AI Search Engines Work
What are the two sources AI search engines use?
Training data, a static snapshot of the internet the model learned from, and real-time retrieval, where the system searches the web via APIs and reads fresh pages. Training data updates roughly every six months; retrieval happens at question time.
What is query fan-out?
It is the expansion of one user prompt into many long-tail sub-queries that run in parallel behind the scenes. Research cited by Ahrefs found an average of 9 to 11 sub-queries per prompt, with some reaching 28.
Can you rank in AI search?
Not in the traditional sense. AI citations are probabilistic, so the same question can produce different cited brands on different runs. The practical goal is AI visibility: raising your share of citations over repeated runs.
Does SEO still matter for AI search?
Yes. Ahrefs reports that 76% of AI Overview citations come from pages already ranking in Google's top 10. Backlinks, rankings, and content quality directly influence real-time retrieval.
Should I optimize for fan-out queries as keywords?
No. Fan-out queries are synthetic, inconsistent, and over 95% have zero search volume. Use them as a map of the topics the AI considers important, then cover those topics thoroughly in your content.
How fresh does AI-cited content need to be?
Ahrefs found AI-cited content runs about 25% fresher than typical SERP results. Updating pages when facts change and dating your claims is one of the few direct levers you control.
Can small brands appear in AI answers at all?
Yes. 14% of pages cited in AI Overviews do not rank in Google's top 100, and ChatGPT's overlap with Google is even lower. Consistent brand mentions and full topic coverage can outweigh weak traditional rankings.
What is RAG in simple terms?
Retrieval-augmented generation means the model searches the web for relevant pages at question time, reads them, and grounds its answer in what it found, instead of relying only on memorized training data.
Which tools show me the queries behind AI answers?
The AI Responses report in Ahrefs Brand Radar surfaces the fan-out queries behind ChatGPT and Perplexity prompts, giving you a window into the topics the AI considers important.
From Watched Lessons to Written Knowledge
The core insight of this lesson is that understanding the mechanics of AI search turns scattered tactics into a coherent strategy. That same logic applies to the knowledge sitting in your own videos: a clear explanation, once transcribed and structured, is far easier for both readers and AI systems to find and cite. Some creators already work this way; Dev Doido do canal do youtube, for example, publishes transcript-based written versions of video lessons alongside tools like CrazyStack.
If you have lessons like this locked inside YouTube content, that is exactly the gap Skala Blog closes. It turns a video URL into a transcription and then into a structured written article, so the ideas you explained once can keep working in search. AI engines cite what they can read; giving your best explanations a written home is how they get read.
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