You do not have to be technical to make good calls about AI search. But a rough model of how these engines work explains the rest: why crawler access matters, why structure matters, why freshness matters.
Two ways an engine knows things
An AI engine answers from two different places, and the difference matters.
- From memory (training). The model was trained on a huge amount of text up to a cutoff date. When it answers from memory, it is drawing on patterns it learned then. It cannot cite a live page, and it can be out of date.
- From live retrieval (search). When the engine fetches current web pages to answer, it can quote them and cite them. This is where you can show up today, with attribution.
OpenAI’s own setup makes the split concrete: GPTBot gathers content for training, while OAI-SearchBot and ChatGPT-User fetch live pages to build cited answers in ChatGPT search. They are different crawlers doing different jobs, which is exactly why your crawler access settings can let you into one and not the other.
How a live answer gets built
Most answer engines, ChatGPT search, Perplexity, and Google’s AI Overviews, follow the same three-step shape:
- Retrieve. The engine searches an index for pages relevant to the question. It usually reads many more than it will cite.
- Synthesize. A language model writes an answer grounded in those retrieved pages.
- Cite. It attaches a few sources that support the answer.
This pattern has a name: Retrieval-Augmented Generation, or RAG, introduced in a 2020 research paper. AWS puts it plainly: RAG makes a model “reference an authoritative knowledge base outside of its training data sources before generating a response,” and that output “can include citations or references to sources.” Retrieve first, then answer from what was retrieved.
The tying-to-sources step is called grounding. It is how engines stay current and reduce hallucination (confident answers that are simply wrong). Google offers “Grounding with Google Search” for exactly this reason: anchor the answer to real, retrievable pages instead of the model’s memory alone.
Query fan-out: one question becomes many
One wrinkle changes how you think about content. Google’s AI Mode uses a technique it calls, in its own words, query fan-out: it “issues multiple related searches concurrently across subtopics and multiple data sources and then brings those results together.” Your buyer asks one question; the engine quietly asks a dozen smaller ones.
The practical takeaway: you are not optimizing for a single keyword anymore. A page that thoroughly answers the sub-questions around a topic has more surfaces to get pulled in on. (The exact number of sub-queries is not something Google publishes, so ignore any blog that quotes a precise figure.)
Why structure and brevity matter
Models read in tokens, small chunks of text (a token is roughly four characters, or about three-quarters of a word). Every model has a fixed context window: a hard limit on how many tokens it can hold at once, shared across the question, the retrieved pages, and the answer it writes.
That limit is the reason clean structure pays off. Cluttered, verbose, JavaScript-dependent pages cost more tokens to convey the same fact, and leave less room for an engine to find and lift the part that answers the question. This is a reasonable inference from how context windows work, not a published rule, but it lines up with everything else: clean, well-structured, extractable content is easier for an engine to use.
What this means for you
Every other guide is a consequence of this model:
- The engine has to reach your content, so crawler reachability and access matter.
- It has to understand you, so structured data and a clean site map for AI matter.
- It prefers to cite current, trustworthy, well-structured sources, so freshness, authority, and writing for extraction matter.
None of it is magic. It is a machine reading the web on your buyer’s behalf. Your job is to be easy to read.