LLM Optimization: A Practical Guide to Getting Named in ChatGPT and Claude Answers

LLM optimization is the practice of shaping what a website says, and how clearly it says it, so a large language model becomes more likely to name that brand instead of a competitor when it writes an answer to a buyer’s question. It sits next to search engine optimization but points at a different target: a search engine returns a ranked list of links, while a model writes a paragraph in its own words and decides, per question, whether your name belongs inside it.

What LLM optimization actually changes on a page

Two layers are involved, and they are not interchangeable. The first is substance: does the page state a specific, quotable fact in plain language, or does it talk around the subject in marketing phrasing that gives a model nothing concrete to lift? The second is structure: does the page mark up that fact so a crawler and a model can parse it unambiguously, through clean headings, explicit entity names instead of vague pronouns like “our platform,” and schema markup that states the same fact in a machine-readable form. Structure without substance is a well-formatted page with nothing worth citing. Substance without structure is a good fact buried where a model has to work to find it. For a fuller walk-through of both layers, see what LLM SEO covers in practice.

Which models does LLM optimization need to account for

There is no single “the LLM” to optimize for. FoundCite checks answers across five engines: ChatGPT, Claude, Perplexity, Grok, and Google AI Mode, and they do not source their answers the same way. Some lean heavily on what a model absorbed during training, with no live lookup involved. Others, Perplexity in particular, run a real search on nearly every question and write the answer from pages read at that moment. Optimizing for one mechanism does not automatically optimize for the other: a page can be well-represented in a model’s training data and still lose out on a retrieval-grounded answer if it is not the kind of page a live search would surface right now, and vice versa. That split, and what it means for where to put effort, is covered in more depth in how AI visibility gets measured across engines.

Why traffic is not a proxy for whether it is working

The instinct is to assume the sites with the most traffic get cited the most, the same way domain authority correlates loosely with search rankings. A direct comparison inside the AI search visibility tracking category shows that assumption breaking. SE Ranking draws roughly 320,422 visits a month and was named 4 times in a set of scanned peer mentions. Peec AI draws roughly 1,132 visits a month, about 283 times less traffic, and was named 7 times in the same set, more often than the much bigger site. Traffic measures how many people already found a site through search. It says nothing about whether a model decided that site was worth quoting in an answer it generated on its own. That is a separate thing to optimize for, not a byproduct of the first.

Content optimization vs technical optimization: where the leverage actually is

Of the two layers above, content substance carries more of the weight than most teams expect going in. Adding a clean schema block or a well-formed llms.txt file takes an afternoon and helps a model parse a page faster once it gets there. It does nothing to make a thin page worth quoting in the first place. The harder, higher-leverage work is rewriting the actual prose: stating the specific claim in the first sentence instead of building up to it, naming your product and your competitors explicitly instead of hiding behind “our solution,” and writing the kind of concrete, checkable detail that gives a model something worth lifting into a generated answer. A page optimized only at the technical layer is easy to spot: perfect markup, vague content, zero citations.

A concrete starting checklist

In order of what tends to matter most first:

• Rewrite the first sentence of every important page. State the specific claim or answer immediately, before any framing or context. A model extracting a citable fact rewards a page that gets to the point.
• Replace vague self-reference with named entities. Swap “our platform” and “this solution” for the actual product name, and name competitors by name where a comparison is genuinely being made. A model cannot quote a pronoun.
• Add structured data once the content itself is specific. The schema markup generator builds the JSON-LD version of the same facts your prose already states, so a crawler does not have to infer them.
• Get named on pages you do not own. Vendor comparison posts, review sites, documentation, and industry pages all feed into what a model has read or can retrieve. A homepage rewrite alone will not move a citation that gets built from a source outside your own domain.
• Check more than one engine before declaring anything fixed. A change that moves a ChatGPT answer can leave a Perplexity answer untouched, since one leans on training data and the other on live retrieval.

None of these are one-time tasks. A page edited once and never revisited drifts out of date the moment a competitor publishes something more specific, which is why optimization is closer to an ongoing practice than a project with a fixed end date. For how a single score attempts to summarize the state of all of this at once, and why the same brand can score differently in two different tools, see what a GEO score actually measures.

Does LLM optimization replace SEO, or sit alongside it

Alongside it, not instead of it. Search traffic and AI answers are two separate surfaces where a buyer can first encounter a brand, and a page that ranks well in Google is not automatically the page a model chooses to quote, since the two systems are asking a different question about the same content: one asks “does this page match the query well enough to rank,” the other asks “does this page state something specific enough to be worth quoting in an answer I’m about to write.” Most of the tactics reinforce each other, clear structure and specific, well-supported claims help both, but treating LLM optimization as a checkbox added on top of an existing SEO strategy, rather than content decisions made with both audiences in mind from the start, is how a page ends up ranking fine and never getting named.

How to actually know if any of this worked

Every change above is a guess until it is checked against real answers. The only reliable method is asking the assistants the questions your buyers actually ask, in a fresh session with no memory of your brand, across more than one engine, and reading what comes back. A single check on ChatGPT tells you about ChatGPT. It tells you nothing about whether Claude or Perplexity would answer the same question the same way, and the SE Ranking versus Peec AI gap above is exactly the kind of difference a single-engine check would miss entirely.

That is the specific gap FoundCite is built to close: one run checks the same buyer question against ChatGPT, Claude, Perplexity, Grok, and Google AI Mode side by side, so you see where a brand is already being named and where it is invisible, instead of inferring it from a traffic number that, as the numbers above show, does not track citations at all. See how the checks and plans are structured on the pricing page.