How AI Search Engines Actually Work — And What It Means for Your Content
A deep dive into fan-out queries, probabilistic retrieval, and why keyword strategy for AI is nothing like traditional SEO.
Nisha Rinesh
5/19/20265 min read
Search & AI · Long Read
The way people find information is changing faster than most marketers and publishers realise. For two decades, Google's ten blue links were the lingua franca of the web. Then came featured snippets, voice answers, and knowledge panels. Now we are in a new era - one where an AI reads, reasons across dozens of sources, and synthesises a direct answer before the user even scrolls.
To stay visible in this new world, you need to understand three emerging disciplines: AEO (Answer Engine Optimisation), GEO (Generative Engine Optimisation), and LLMO (Large Language Model Optimisation). Each is a response to the same underlying shift: search is no longer a lookup. It is a reasoning task.
From Ten Blue Links to AI Synthesis
Traditional search was transactional and simple: you typed a query, you received a ranked list of URLs in the SERPs (search engine results pages), and you chose where to click. The search engine's job was essentially a glorified index - match keywords, rank by authority, and display.
AI search engines work differently at a fundamental level. They use two complementary approaches to generate their answers:
Two retrieval modes
1. Training data knowledge - The model was trained on vast corpora of text and internalised facts, relationships, and writing patterns. When you ask it something, it can answer from memory - much like a human expert recalling what they studied.
2. Real-time retrieval (RAG) — Retrieval-Augmented Generation lets the model fetch live web content, index it on the fly, and ground its answer in up-to-date sources. This is where your SEO still matters: ranking in Google's SERPs, earning backlinks, and publishing quality content all increase the probability your page gets pulled into the RAG pipeline.
The implication is significant. Your content needs to win in two arenas simultaneously: it must rank well enough to be retrieved, and it must be structured clearly enough that an AI can extract a confident answer from it.
The Fan-Out Query: Search's Quiet Revolution
Here is where the mechanics get genuinely fascinating - and where most marketers are caught completely unprepared.
Traditional search was a single-shot transaction. You asked one question; you got one results page. Even as search evolved into multi-query sessions (searching, refining, searching again), each query was a discrete human act.
"AI search doesn't wait for you to refine your query. It does that work itself - at machine speed, across dozens or hundreds of sub-questions."
Modern AI search systems decompose your single question into a fan-out of parallel sub-queries. The system figures out every sub-topic it needs to understand in order to produce a comprehensive, accurate answer - and it fires all of them, often simultaneously.
Think about that last number. When a user asks ChatGPT Deep Research to "research the best podcast hosting platforms," it is not running one search. It is running hundreds - drilling into pricing, reliability, distribution reach, monetisation features, audience analytics, customer support reputation, and more. Each of those sub-queries may surface different pages, different authors, different sources.
The Characteristics of Fan-Out Queries
This has four important properties that every content strategist needs to understand:
- Synthetic: These sub-queries are generated by the AI, not typed by humans. They do not appear in any keyword planner. You cannot find them with traditional research tools.
- Inconsistent: The same user question asked twice may produce a different fan-out. The AI determines what to look for based on context, model state, and the moment of generation.
- Zero search volume: Around 90% of fan-out queries are never typed by real people. They exist only in the AI's reasoning process. This means the SEO playbook of "target high-volume keywords" is largely irrelevant for AI visibility.
- Probabilistic: AI does not guarantee which sources it will retrieve. It makes probabilistic choices based on apparent authority, topical breadth, content structure, and recency. Your goal is to increase the probability your content is selected - not to guarantee it.
Why AEO Is Fundamentally Different from SEO
Traditional SEO was page-centric. You picked a target keyword, you optimised one URL for that keyword, you built links to that page, you ranked. Success meant one page, one query, one SERP position.
AEO - demands a niche-wide perspective. Because AI fan-out queries can span every corner of a topic, your entire content ecosystem is being evaluated, not just individual pages. If your site covers a topic incompletely, the AI will source the missing pieces from a competitor who covers it fully.
A Concrete Example: The Podcast Page Test
Imagine a user asks an AI: "How to create an automated agent?" The AI fans out into sub-queries covering basics, equipment, hosting, promotion, and more. Now compare two competing pages:
When an AI search engine fans out its queries on the topic "how to create an automated agent," your competitor's page satisfies far more of the sub-queries. The AI will cite that page - not yours - even if your page ranks equally well in traditional search. Topical completeness is the new competitive moat.
Enter AEO, GEO, and LLMO
These three disciplines have emerged to address the challenge of AI visibility. They overlap considerably but each has a distinct emphasis:
AEO - Answer Engine Optimisation: Structuring content so AI systems can extract direct, citable answers. Focuses on question-answer formatting, entity clarity, schema markup, and topical authority. AEO treats AI as the primary audience, not just a secondary reader.
GEO - Generative Engine Optimisation: Optimising for inclusion in AI-generated responses specifically from large language model-powered engines (like Google AI Overviews or Perplexity). GEO practitioners study which content attributes — statistical claims, source authority, structured formatting - correlate with citation in generated answers.
LLMo - Large Language Model Optimisation: The broadest framing: ensuring your brand, content, and expertise are well-represented in the training and retrieval data that LLMs draw from. This includes long-term brand signals, high-quality link profiles, and consistently authoritative content that gets indexed, cited, and eventually learned.
What This Means in Practice
The strategic implications are significant. If you are building a content programme for AI-first visibility, the playbook looks different at every level:
Keyword strategy becomes niche mapping. Instead of targeting individual high-volume terms, you map every question your audience could plausibly ask across the entire topic. You then audit your existing content against that map and fill the gaps.
Single pages become topic clusters. A well-ranked but isolated page that covers only one facet of a topic will be supplemented - or replaced - by a competitor who covers all facets. Internal linking, hub-and-spoke architecture, and breadth of coverage matter more than ever.
Traditional SEO still underpins everything. Because RAG-based AI systems retrieve from the live web, ranking in Google remains foundational. Backlinks, Core Web Vitals, and E-E-A-T signals are not obsolete - they are the pipeline that gets your content into the AI's retrieval window in the first place.
The shift from ten blue links to AI-synthesised answers is not an incremental change. It is a structural one. The sites that thrive will be those that think like an expert answering every possible question in their field - not like a marketer targeting the highest-traffic keywords on a spreadsheet.
The search landscape is still evolving rapidly. Fan-out query counts, RAG pipeline designs, and AI citation behaviours will continue to shift. What will not change is the underlying principle: AI rewards completeness, clarity, and authority. Build for that, and you build for whatever comes next.