How AI Search Engines Discover and Reference Content

AI search tools answer questions by combining two things: knowledge a language model absorbed during training, and documents retrieved from a search index at the moment you ask. The retrieved documents are what usually get cited or linked, and they are found by crawlers much like traditional search. So for most businesses, being discoverable in AI search starts with being crawlable, indexable and clearly written — the same foundations as ordinary SEO.

Two sources of knowledge

Training data. A large language model is trained on a vast body of text gathered up to a cut-off date. This gives it general knowledge and language ability, but it is frozen in time, it does not reliably remember specific sources, and you cannot update it by editing your website today.

Live retrieval. When an AI search product needs current or specific information, it runs searches, pulls back relevant pages, and passes passages from them to the model as context for the answer. This approach is often called retrieval-augmented generation. Because the answer is grounded in fetched documents, the product can show which sources it used.

For a business, live retrieval is the part you can influence. If your page is in the index, relevant to the question and clearly written, it can be one of the documents the system considers.

The retrieval pipeline, step by step

The details differ between products and are not fully published, but the general flow looks like this:

1. Understanding the question. The system interprets what is being asked, often taking earlier turns of the conversation into account.
2. Expanding into searches. A complex question may be broken into several related searches. Google has described a "query fan-out" technique in AI Mode that issues multiple related searches across subtopics.
3. Retrieving candidates. Each search returns documents from an index — Google's own for its AI features, and a mix of in-house and partner indexes for other tools.
4. Selecting passages. The system picks the passages that best address each part of the question.
5. Generating the answer. The model writes a response using those passages, and the interface attaches links or citations.

Two practical lessons follow. Because questions get broken into sub-questions, a page that covers the related angles of a topic gives the system more to work with. And because passages are selected individually, each section of your page needs to make sense on its own — see how to structure content so AI answers can use it.

Who is crawling your site

Each AI company runs its own crawlers, often with separate names for different purposes. A few widely documented examples:

User agent or token	Operator	What it is used for
Googlebot	Google	Google Search, including AI Overviews and AI Mode
Google-Extended	Google	A robots.txt token controlling use of content for Gemini model training and grounding; Google states it does not affect inclusion in Search
Bingbot	Microsoft	Bing search, which also supports Copilot's web answers
OAI-SearchBot	OpenAI	Surfacing sites in ChatGPT search features
GPTBot	OpenAI	Collecting content that may be used for model training
PerplexityBot	Perplexity	Building Perplexity's search index

Names and purposes change, so check each provider's own documentation before making decisions.

What you control

robots.txt

You can allow or disallow each crawler in robots.txt. The important decision is whether to treat training crawlers and search crawlers differently. Some businesses block training crawlers but allow search crawlers so they can still be surfaced and linked in AI search. Blocking a search crawler generally means that product cannot fetch your pages for answers. Note that robots.txt is a request that reputable crawlers follow, not a security control.

Firewalls and bot protection

Security tools and CDNs increasingly offer settings to block AI crawlers, and some may block them by default. If you want to appear in a given AI tool, confirm that your hosting and security settings are not quietly turning its crawler away. Your server logs will show which bots are reaching you.

Rendering and content format

Some crawlers handle JavaScript less fully than Googlebot. If your key content only appears after scripts run, or sits inside images or PDFs, it may be missed. Server-rendered HTML text is the safest format. Our guide to crawlability and indexing explains how to check what search engines actually see.

Why answers vary

The same question can give different answers because:

• The model generates text with some built-in variation.
• Retrieval results shift as indexes update.
• Location, language, signed-in state and conversation history change the context.
• Products are updated frequently, sometimes changing how sources are chosen.

This is why measurement has to look at patterns across many checks; see how to measure your visibility in AI search.

Common misconceptions

• "The AI already knows about us, so content changes won't matter." Training knowledge is dated and vague; live retrieval is where current, specific information comes from.
• "A special file will get us included." Google's guidance states there are no additional requirements for its AI features beyond normal Search eligibility. Proposals such as llms.txt are optional and unofficial, with no confirmed effect on citations.
• "Being cited means we rank first." Systems can draw on pages that are not the top organic result, and the reverse is also true.

Key takeaways

• AI search mixes frozen training knowledge with live retrieval; citations come mainly from retrieval.
• Retrieval depends on crawlers and indexes, so crawl access and indexability come first.
• Decide deliberately which AI crawlers to allow, and check that security settings match that decision.
• Write pages whose individual sections can stand alone.

If you would like us to check how AI crawlers see your website and what to change, see our AI search optimisation services.