LLM SEO: how to optimize content for LLMs

That makes LLM optimization less mysterious than it sounds: clear, specific, current pages that the right crawlers can reach.

This guide is part of our AI search optimization series.

What LLM SEO means

LLM SEO means optimizing web content so that language models, and the AI search products built on them, can retrieve it, trust it and cite it accurately. The term overlaps almost entirely with generative engine optimization; people tend to say "LLM SEO" when they're thinking about the model, and "GEO" when they're thinking about the search product.

Either way, the foundations are SEO foundations. Google's AI optimization guide says optimizing for generative AI search "is optimizing for the search experience, and thus still SEO." What shifts is the emphasis: more weight on quotable passages, factual precision, entity clarity and freshness, and less on the rank of a single blue link.

Training data vs live retrieval: two ways an LLM knows your page

An LLM knows your page either because the page was in its training data or because a search step retrieved the page while it was answering. The two paths have different crawlers, different timelines and different levels of control.

Nobody controls what ends up in a model's training set. Live retrieval is where your work pays off, because it's how a model answers beyond its training cutoff and how it produces linked citations. Google's own documentation describes Gemini's grounding feature the same way: when the model decides a search would help, it runs Google searches and cites the URLs it gets back, giving answers "beyond its knowledge cutoff" (Gemini API docs).

Live retrieval runs on crawled, indexed pages

Live retrieval runs on crawled, indexed pages because every major AI search product pulls candidates from an index before the model writes a word. If a page isn't in that index, or the index holds an old copy, the model can't quote the current version.

• Google AI Overviews and AI Mode draw on Google's Search index. A page "must be indexed and eligible to be shown in Google Search with a snippet" (Google Search Central).
• ChatGPT search uses OAI-SearchBot plus "third-party search providers" and partner content, according to OpenAI's launch statement (PYMNTS, October 2024). Microsoft announced Bing as ChatGPT's default search experience in May 2023 (Bing blog); OpenAI hasn't named its current providers.
• Perplexity runs its own index of more than 200 billion URLs (Perplexity).
• Copilot is grounded in Bing's index. Bing's AI Performance report, a public preview since February 2026, shows the "grounding queries" used to retrieve cited pages (Bing Webmaster Blog).

We break down each engine in where ChatGPT gets its information.

One nuance: user-triggered fetchers such as ChatGPT-User and Perplexity-User can read a known page live while answering. That helps once an engine already knows your URL. It doesn't replace discovery, and OpenAI says ChatGPT-User "is not used to determine whether content may appear in Search" (OpenAI crawlers doc).

Write passages an LLM can quote: self-contained, specific, sourced

A passage an LLM can quote makes sense on its own, says something specific, and shows where the fact comes from. Engines lift short spans of text, so each span has to carry its own context.

• Self-contained. Start sections with a sentence that names the subject: "IndexNow is an open protocol for…" rather than "It lets you…". A model reading only that paragraph should still understand it.
• Specific. Prefer numbers, names, dates and conditions over adjectives. "Google says crawling can take a few days to a few weeks" is quotable; "indexing can be slow" isn't.
• Sourced. Link the primary source next to the claim. In the original GEO study, adding quotations (about 41%), statistics (about 31%) or source citations (about 28%) raised a page's visibility in answers from a simulated engine (Aggarwal et al., arXiv 2311.09735).
• Matched to the question. AirOps research on ChatGPT citations, as reported in Kevin Indig's Growth Memo (March 2026), found pages with at least 50% title-to-query overlap had a 20.1% citation rate, against 9.3% for pages under 10% overlap (Growth Memo).

Keep expectations honest. A NeurIPS 2025 benchmark found most on-page "conversational SEO" rewrites had little or negative effect, while the page's position in retrieval, which ordinary search ranking drives, mattered far more (C-SEO Bench, arXiv 2506.11097). Writing quotable passages helps a retrieved page get used. It doesn't get the page retrieved.

Entities, consistency and brand mentions

Entities, consistency and brand mentions matter for LLM SEO because models build their picture of your company from how it's described across many pages, not just your own. Inconsistent names, descriptions or facts make that picture blurry.

• Use one name and one short description for your company and product everywhere: your site, author pages, directory profiles, structured data and social bios.
• Keep facts consistent. Prices, founding dates and feature lists should match across pages, or a model may quote the wrong one.
• Earn independent mentions. In an Ahrefs study of 75,000 brands (vendor study, 2025, AI Overviews only), brand web mentions correlated with AI Overview visibility at ρ = 0.664, compared with 0.218 for backlinks (Ahrefs). That's a correlation, not a guarantee, but it's the strongest signal in the data so far.

A mention only counts once the page it appears on has been crawled. An unindexed review or guest post is invisible to every model's retrieval step.

Technical access: robots.txt, AI crawlers and rendering

Technical access for LLM SEO comes down to letting search crawlers in, deciding separately about training crawlers, and making sure your important content is in the HTML those crawlers read. Each AI company documents its own user agents, and each setting is independent.

Practical rules:

1. If you want to limit training, block GPTBot, not OAI-SearchBot.
2. Check your CDN and firewall as well as robots.txt. Google lists "ensuring that crawling is allowed in robots.txt, and by any CDN or hosting infrastructure" as a basic.
3. Allow time. OpenAI says robots.txt changes take about 24 hours to apply to search; Perplexity says up to 24 hours.
4. Put important content in server-rendered HTML. Google recommends "making sure that important content is available in textual form," and the AI search crawlers' documentation doesn't describe JavaScript rendering, so don't rely on it.

What about llms.txt? It's a proposed file that summarizes your site for language-model tools. Google's guide says Google Search ignores such files, and the OpenAI and Perplexity crawler docs don't mention it. It's cheap to add, but it isn't an access control or a ranking factor.

Keep it current: update, then get it re-crawled

Keeping content current for LLMs is a two-step job: make a real update, then get the updated page re-crawled so the index, and every answer built on it, holds the new version. Skipping the second step is how a model ends up quoting last year's pricing.

The evidence favors maintained pages over constant new publishing. In Seer Interactive's 2026 vendor study (7,683 pages cited by ChatGPT, Gemini and Perplexity for four brands), 75% of cited pages had been updated within a year, but only 42% of those with both dates readable had been published within a year (Seer Interactive). Established URLs that are kept up to date did best. Read more in content freshness.

To make updates count:

1. Change the substance: facts, numbers, examples, screenshots. A new date on unchanged content isn't an update, and Google's guidance specifically warns against it.
2. Set sitemap lastmod to the real change time. Bing asks for exactly that (Bing Webmaster Blog).
3. Ask for a recrawl, then confirm the crawler actually came back. Google says crawling "can take anywhere from a few days to a few weeks" (Google).

FreshlyCited handles step three for Google, whose AI Overviews and AI Mode link only to indexed pages. It checks which of your URLs Google has indexed, sends the ones it hasn't, and re-checks on days 1, 3 and 7 so you can see the result. For an updated page Google already has, choose Refresh anyway to send it: that uses 1 index credit when it's sent, with no credit back, because we can't see when Google re-crawls a page it already has. For sites you verify with an IndexNow key file, the same URLs also go to Bing through IndexNow, free; Microsoft says Copilot's web answers use the Bing search service. ChatGPT, Perplexity and other assistants use their own crawlers and search partners. We don't submit to them, and we don't claim to.

LLM SEO tools: what's worth measuring

The LLM SEO tools worth paying for measure outcomes you can act on: whether you're cited, whether you're mentioned, whether AI assistants send traffic, and whether your pages are crawled and indexed in their current form. Tools that only score a page for "AI readiness" are harder to tie to results.

Two cautions. First, crawling isn't citation. In an experiment reported in Kevin Indig's Growth Memo (March 2026), a thin, brand-new 60,000-page AI-generated test site built by Metehan Yesilyurt drew more than 29,000 GPTBot requests in its first 12 hours, against 11 from Googlebot. GPTBot collects training data, so that volume says nothing about whether the site will be cited (Growth Memo). Second, citations are volatile and engine-specific, so measure trends over weeks, not single checks. For a rundown of options, see our list of LLM SEO tools.

FreshlyCited isn't a citation tracker and doesn't rewrite content. It covers the step after you publish or update: helping Google find and index the new version and showing you what Google has indexed, with an API on any pack or Agency plan (and an MCP server on Agency Pro and Agency Scale) if you want it inside your own workflow. If a page you've improved isn't showing up, get updated pages re-crawled and check again. Check your own links free.