How does answer engine optimization actually work?

Google's developer documentation gives a worked example. In its description of grounding with Google Search in the Gemini API, the model starts by reading the request and judging whether looking something up would make its reply better. When it decides to search, it comes up with its own search terms, sometimes several sets, and sends them. It then reads what comes back, combines it into a reply, and marks which sources back which passages.

Two details in that example matter to anyone who publishes. The search is conditional, because the model runs it only when it judges that a search will help. And the model writes the queries, so they can differ from what the user typed. Google's guide for site owners describes the same idea in AI Overviews and AI Mode and calls it query fan-out. For a question about fixing a weedy lawn, it says the related searches might cover herbicides, chemical-free removal and prevention. Your page can be retrieved for one of those narrower searches even if it doesn't match the wording of the original question.

That sequence is documented for one Google developer product, and other engines don't necessarily follow the same steps. Even within Google Search, AI Overviews and AI Mode can use different models and techniques, so their responses and links vary.

For Google's own search features, retrieval draws on the same Search index and core ranking systems as ordinary results. That's why Google says optimizing for generative AI search is still SEO from its point of view. Our answer on the difference between AEO and SEO covers where the two part ways on other engines.

Training is where text collected in the past shaped a model. Retrieval is where an engine fetches pages when a question is asked. They aren't the only ways a company ends up in an answer, but they're the two that site controls address. For citations in search-backed responses, retrieval is the one that matters. It's what can lead to a cited link, and it can change when you change a page.

The engines keep the two apart in their controls. OpenAI's crawler documentation lists OAI-SearchBot for surfacing sites in ChatGPT's search features and GPTBot for collecting content that may be used in training. It says each setting is independent, so a site can allow the first and disallow the second. A site that opts out of OAI-SearchBot won't be shown in ChatGPT search answers, though OpenAI says it can still appear as a navigational link. A third agent, ChatGPT-User, visits a page when a user's request calls for it and isn't used for automatic crawling.

Google separates them too. Its Search generative AI control in Search Console decides whether a site's links and content can appear in Search generative AI features. Google says that control doesn't affect AI training, and it points to Google-Extended for that.

So under OpenAI's documentation, a rule that blocks the training crawler doesn't remove a site from ChatGPT search answers. For citations, check the search setting on each engine you care about.

For Google, access means a page is indexed and eligible to show with a snippet, on a site included through the Search Console control. Google's page on AI features lists supporting practices: crawling allowed in robots.txt and by any CDN or hosting layer, important content available as text, and pages that can be found through internal links. For ChatGPT search, OpenAI recommends allowing OAI-SearchBot in robots.txt. These settings are worth reading on any site, because a rule added for another reason can also block a search crawler.

Google's guide puts the evidence on the page first: content people find unique, compelling and useful is likely to influence presence in generative AI search more than its other suggestions. It contrasts commodity content, which restates what anyone could say, with first-hand material. It also lists things Google Search doesn't require: special files such as llms.txt, pages chopped into small chunks, wording written for AI, or any particular schema markup. We go into the markup question in the role of structured data in AEO.

Fan-out shows how first-hand detail becomes useful. Suppose a buyer asks an engine which CMS suits a 20-person B2B software company. Suppose the engine splits that into searches about pricing, migration effort and how the options compare. A page that says what one migration involved, how long it took and what it cost has specific information to offer the migration search. A page that repeats the general pros and cons offers what many other pages already say. The engine still makes its own choice, but the first page brings information that isn't available elsewhere.

Lean Labs, a HubSpot agency, describes its AEO work as covering both areas. It chooses buyer questions from sales calls, CRM insights and Search Console demand, then publishes expert-led answers in HubSpot with supporting proof. Alongside that, it configures the site and resolves technical friction. Then it tracks prompts and citations to see what changed. The order is worth copying, because it starts from the questions buyers ask.

These two areas cover your own site. Mentions and sources elsewhere on the web are a further area, which we cover in what AEO covers beyond website content.

Google's guide says a page can meet every requirement and best practice and still not be crawled, indexed or served. Answers also vary between engines and over time. HubSpot's documentation for its AEO tracking tool notes that each engine may generate different responses and cite different sources for the same prompt, and that responses change over time.

So a promise of a citation for a given prompt goes beyond what these documents support. Which engines to work on is a separate decision, covered in which AI platforms an AEO strategy should target.

Because answers vary, one check of one prompt tells you little. HubSpot's tool runs tracked prompts daily, and HubSpot advises reviewing results across multiple days or weeks before reading a trend. For Google, Search Console's generative AI performance report counts impressions in Search generative AI features. An impression there is a link to your site shown to a user, and the report groups the count by page, country, date or device.

Those figures measure visibility. A shown link doesn't mean someone clicked it, so visits and CRM results need their own reports.

The work then repeats. You pick the questions, publish the evidence and check access. You watch a fixed set of prompts and the impression report for several weeks, and use what you see to choose the next page to improve. Tracking citations, visits and contacts as separate numbers shows which of them moved after a change. Our answer on how long AEO takes to show results covers the timing.