LLM Search Optimization: How AI Engines Find Your Pages

LLM (large language model) search optimization is the work of making your pages easy for ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews to find, read and cite when they search the web to answer a customer's question.

That search step is where your current pages get their chance to be in the answer. In a Pew Research Center survey of 5,119 U.S. adults in February 2026, about four in ten said they use chatbots for information searching. When one of them asks who to hire in your field and the engine's search never reaches your page, the answer gets built from other sources. Your own page, with your services and fees in your words, never gets to contribute. You don't get a missed call or a lost click to look at.

Key points

• Engines answer from what the model learned in training and from what they find by searching when the question is asked. LLM search optimization works on the second.
• One question can turn into several searches. Google calls this query fan-out, so it helps to cover the likely follow-up questions on your pages.
• Google says its AI search features need no special files or schema markup, so the work starts with pages it can crawl, index and understand.

What is LLM search optimization?

Two terms come up throughout. A crawler is the program an engine sends to fetch pages from websites. A search index is the engine's stored collection of the pages its crawler fetched, which it searches when a question comes in. The work covers whether the crawler can reach your pages, whether they turn up in those searches, and whether each page answers plainly.

The term overlaps with generative engine optimization (GEO) and answer engine optimization (AEO), and people often use them for the same work. "LLM search optimization" points at one moment, the search an engine runs before it writes. For the wider picture, our plain guide to generative engine optimization covers the basics.

Where does an LLM get the information in its answer?

From two places, and they run on different clocks.

Anthropic's documentation for Claude's web search tool spells out the split. The tool "gives Claude direct access to real-time web content, allowing it to answer questions with up-to-date information beyond its knowledge cutoff." Claude searches when a request depends on information that is "current, changing, or outside its training data," and answers directly when the request draws on stable knowledge.

The second row matters most to an owner. Anthropic lists "Information about specific organizations, people, or products that might have changed" among the requests that send Claude to search. A question about your firm, your fees or your service area is that kind of question, so in Claude's case the answer leans on what the search finds when it's asked.

Those docs describe Claude as developers reach it through Anthropic's API. Google describes the same split for its own products. Its guide to generative AI features in Search calls the retrieval step grounding, a technique "used to improve the quality, accuracy, and freshness of AI responses."

How does an LLM's search step work?

The engines' own documentation describes the search step in roughly this order.

1. The engine decides whether to search. In Anthropic's docs, "Claude determines when to search based on the prompt."
2. It writes its own searches, often several. Google says AI Overviews and AI Mode may use query fan-out, "issuing multiple related searches across subtopics and data sources." Anthropic's docs say simple factual queries typically use one to three searches, and comparative research can use 10 or more.
3. It pulls pages from a search index. Google says its AI features rely on "our core Search ranking systems to retrieve relevant, up-to-date web pages from our Search index." OpenAI says its OAI-SearchBot crawler "is used to surface websites in search results in ChatGPT's search features," and Perplexity says PerplexityBot does the same job for Perplexity.
4. It reads pieces of pages. Microsoft says Copilot breaks content into smaller, structured pieces, and that ranking in AI search is "more about which pieces of content earn a place in the final answer."
5. It writes the answer and cites sources. For Claude, "The response includes citations for sources drawn from search results."

Hypothetical example: someone asks an engine, "Who's a good accountant near me for a small business that just hired its first employees?" An engine that fans out might search for accountants in that town, payroll setup for a first hire, and what small business accountants charge. A firm with one vague "Services" page may match none of those searches. Take the fee search. If the firm's Services page lists "payroll" but never says how payroll fees are set, the engine has nothing to quote. The edit is one short section under a heading like "How we price payroll for a first hire," saying whether it's a flat monthly fee or billed by the hour, and what it includes.

Which search does each engine run?

Only some engines give you a report to check.

Two of these engines draw on indexes you can already check for free. Google says a page must be "indexed and eligible to be shown in Google Search with a snippet" to appear as a supporting link in AI Overviews or AI Mode. Microsoft's advertising blog says crawlability, metadata, internal linking and backlinks "remain essential for ensuring your content is discoverable" in AI search answers.

Is LLM search optimization just SEO?

Mostly it builds on SEO. Google says its generative AI features "are rooted in our core Search ranking and quality systems," and it says you don't need new machine-readable files, AI text files, markup or Markdown to appear in Google Search. If someone pitches you an llms.txt file as the fix, read what llms.txt is and what it does first.

Three things are new. Fan-out means one buyer question can become several searches, so covering the likely follow-ups is worth the effort. Crawlers outside Google matter, such as OAI-SearchBot and PerplexityBot. And you measure the result in answers, not in a list of rankings.

Microsoft says Copilot works with pieces of pages, while Google says "There's no requirement to break your content into tiny pieces for AI to better understand it." You can satisfy both with normal pages where each section answers one question in its first lines and still makes sense read alone.

What should you fix first?

You'll end with a list of the pages that should answer your buyers' questions, and whether each engine can reach them. The first four steps cost time, not money.

1. Write down five questions buyers ask right before they hire you. Under each one, list the follow-up searches an engine might run, such as price, area served, timing and who the service is for.
2. Find the page that answers each question. If no page does, that's your first gap.
3. Paste each page's address into the URL Inspection tool in Google Search Console, which shows Google's indexed version of that page. You want the status "URL is on Google." Bing Webmaster Tools has its own URL Inspection tool that shows the page's index status in Bing.
4. Open your robots.txt file. It's a short text file that tells search engine crawlers which URLs they can access on your site. Check that it doesn't block OAI-SearchBot or PerplexityBot, the search crawlers for ChatGPT and Perplexity.
5. Before you change any page, ask each engine your five questions. Log the date, the engine, your location, the search mode (such as whether web search was on), the exact question, every business named and every cited link. One answer is a single sample, so ask more than once before you treat a result as a pattern.
6. Put each answer in plain text on the page. Microsoft advises against long walls of text, answers hidden in tabs or expandable menus, and core information kept only in PDFs or images, noting that "AI systems may not render hidden content, so key details can be skipped."

LLM search optimization starts with one gap. Pick the buyer question from step 1 that your site answers worst, then use Vector Index's guide to LLM SEO to see how presence is measured across ChatGPT, Perplexity, Gemini, Claude and Google's AI features, so you can decide which page to fix first.

FAQ

Can I get my business into an LLM's training data?

None of the sources here show a dependable way to do it. OpenAI says its GPTBot crawler collects content that "may be used in training" its models, but no source here measures whether allowing it changes what ChatGPT says about you.

How long do changes take to show up?

OpenAI says that after you update robots.txt, it "can take ~24 hours" for its search systems to adjust. None of the sources here measure how long a page edit takes to appear in an answer, so recheck your questions over several weeks.

Can I see when an AI engine cites my site?

For some engines. Search Console's Generative AI performance report counts impressions, meaning how many times links to your site were shown in AI Overviews and AI Mode. Bing Webmaster Tools has an AI Performance report covering Copilot citations. For ChatGPT, Perplexity and Claude, ask the questions yourself and record the answers.