Human behavior bends, over time, toward less friction: fewer decisions, less wasted effort. Search engines were a leap in that direction — they made information far easier to find. But they never made it easier to interpret. You still got ten blue links and had to do the reading, comparing, and deciding yourself.
AI engines changed that. They don't just retrieve information; they read across many sources, weigh them, and hand back a single, personalized answer shaped to what you actually meant. In effect, they are context machines — absorbing large volumes of information and compressing it down to the intent behind your prompt.
That shift is bigger than it looks. It changes how ordinary people learn about the world, including how they discover brands, educate themselves about them, and decide what to buy. Which is why AI engines are fast becoming a marketing channel in their own right.
But you can't optimize for AI search until you understand how it works. Each engine — ChatGPT, Google's AI Mode, Claude, Perplexity — does it slightly differently, yet they all rest on the same three pillars:
- The user's prompt and context
- The engine's web search — query fan-out and crawlers (AEO)
- Answer generation and construction (GEO)
Let's walk through each.
1. The prompt and context Every time someone talks to an AI engine, they're expressing an intent. These systems are good at reading that intent — not just the literal words, but what sits between them. They combine your question with what they already know about you (from your chat history and context) into a richer picture of what you're really after. That combined understanding is the trigger for everything that follows.
2. The engine's web search - query fan-out (AEO) Here's the part most people miss. The engine doesn't search the web with your exact question. It first expands your single prompt into a set of related sub-queries — a technique called query fan-out. Ask "What's the best CRM for a small B2B team?" and the engine might quietly search for pricing, integrations, ease of use, alternatives, and reviews, all in parallel.
Each sub-query runs through the engine's own crawler and retrieval system — OpenAI's OAI-SearchBot for ChatGPT, Gemini over Google's search index for AI Mode, PerplexityBot for Perplexity, ClaudeBot for Claude. The results flood the engine's working memory with fresh material from across the web.
Getting your content discovered, crawled, and pulled into this retrieval step is what we call AEO — Answer Engine Optimization. If you're not retrievable here, you simply don't exist in the next step.
3. Answer generation and construction (GEO) Now the engine holds a rich pile of retrieved passages from all those sub-queries. The model's job is to interpret them, decide which sources to trust, and assemble the most useful, specific answer to the original question.
This is where your brand either shows up or doesn't — and it's nothing like an SEO rank. "Position 1" means little here. What matters is how your brand appears inside the answer itself:
- Visibility — does your brand get mentioned at all?
- Position — where in the answer does it surface?
- Sentiment — how is it framed?
- Depth (share of voice) — how much of the answer is actually about you?
Optimizing for this final, generated answer is GEO — Generative Engine Optimization.
Why this changes the game Traditional SEO is a race to the top of a list. AI search is subtler: there's no list. There's one answer, and the engine decides which content is genuinely useful enough to retell to the user in their moment of need. You're no longer competing for a rank — you're competing to be the answer.
Now that you understand how an AI search engine actually works, take the first step: try Epitom's AEO & GEO tool.