Up until 2022, everyone was scrambling to get indexed and ranked on the search engine results page. But today, it’s safe to say webmasters’ priorities have shifted. And fairly so, as users continue to switch over to LLMs to find answers.
A key part of AI search visibility is ensuring AI systems can access and interpret your content. That raises a few important questions:
- What’s the “indexed” version of a page for generative AI tools?
- How do LLMs crawl the web to find & generate answers?
These questions have become increasingly urgent as the landscape of information discovery shifts beneath our feet.
Let’s break down how AI systems actually access and retrieve web content.
Why is AI visibility important for brands?
There’s a reason why brands are so keen on looking for answers regarding LLM crawlability. Because ensuring LLMs can “view” your content is a part of optimizing for generative AI.
Here’s why brands should consider investing resources in LLM optimization.
- Outbound referral traffic from ChatGPT to the rest of the web grew 206% in 2025. – Semrush Analysis
- AIOs show up for 51.5% of real user queries and are displayed over organic search results for all. – Study by Cornell University
- AI search visitors will surpass traditional search visitors by 2028. – Research by Semrush
- AI‑referred traffic converts at three times the rate of traffic from other channels. – Microsoft Clarity Blog
What Is LLM Crawlability?
LLMs don’t crawl pages like Google bots. They primarily rely on scraping tools, plugins, and APIs to collect information from web sources.
Their knowledge database generates answers using these two methods.
- Pre-training on massive datasets from books, public internet data, and source code repositories.
- Real-time information retrieval using RAG (Retrieval-Augmented Generation), which fetches content from public web pages at inference time and temporarily injects it into the LLM’s context window to address a user’s question in real time.
The pre-training for each LLM may vary, which is why most of them respond differently and cite different sources when answering the same query. However, the RAG system relies on real-time extraction, meaning it must have some level of crawling involved behind the scenes.
How Do LLMs Crawl the Web?
LLM crawling is variable. And without an official word from manufacturers on how these tools extract information, we can’t say for certain how major LLMs like ChatGPT, Claude, and Perplexity crawl pages. However, a solid hypothesis is that they leverage a combination of third-party crawling and dedicated LLM bots to discover content online.
Via Search Engine Bots
Many generative AI systems rely partly on existing search infrastructure, especially Google and Bing-powered retrieval systems, alongside proprietary crawlers and APIs.
So when LLMs scrape content, they’re essentially counting on search engine bots to have discovered pages, so they can too. Here’s how Google’s official guidelines for AI optimization endorse the importance of crawlability.
“To maximize your site’s visibility in generative AI search features, ensure your content is crawlable.”
This means ensuring your content isn’t blocked by robots.txt and enriching it with internal and external links to allow faster discovery, crawling and indexing.
Via LLM Bots
Many LLM platforms now operate their own crawlers and retrieval systems, while still heavily relying on existing web infrastructure and search indexes.
Note: Unlike Google’s continuously refreshed indexing system, AI retrieval is often more selective, query-driven, and dependent on external search or retrieval frameworks.
Some AI crawlers you should allow on robots.txt file include:
- OpenAI’s GPTBot
- Perplexity’s PerplexityBot
- Anthropic’s ClaudeBot
Filter your log files for user agents like GPTBot, PerplexityBot, and ClaudeBot to see how often LLM crawlers land on your site.
While retrieval systems differ between platforms and LLMs don’t index pages at the same scale as Google, they do operate in their own capacity to find information online.
Important! Unlike Googlebots that automatically discover new pages, follow internal links, and index content, LLM bots needs prompting to discover new pages. It needs humans to search for a specific query before it can navigate the internet to find an answer. For example, imagine you recently posted a blog about Google’s latest guidelines on AI search optimization. In order to get an LLM to discover this new page, a manual action may be required. For example, a user asking about this topic specifically. Something like:
- What do Google guidelines for AI optimization say about llms.txt?
- What are some myths regarding AI search optimization?
- Is SEO relevant for AI optimization per Google’s guidelines?
These questions will prompt the LLM to search for a page that primarily addresses this relatively new topic.
What is the role of llms.txt?
LLMs.txt is a proposed standard by Jeremy Howard. The proposal recommends creating a markdown file pointing LLMs to the important pages or content on a website. This machine-readable markdown file is designed to help AI better understand the context of a page.
Now, Claude has released their own markdown file in their resources section for AI ingestion. But Google’s latest take on AI optimization contradicts this. The document explicitly mentions:
“You don’t need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search.”
Ultimately, llms.txt remains an unofficial proposal rather than a formally recognized search standard adopted by major LLM platforms like ChatGPT, Claude, or Perplexity.
Takeaway?
So, how do LLMs crawl the web? Rather than independently crawling the entire internet in real time, LLMs typically rely on existing web data and retrieval frameworks such as RAG and query fan-out to access relevant information from web pages. They also depend on traditional search engine indexes, manual prompting, APIs, and overall web accessibility to scrape sources to cite.