What is LLM optimization?

LLM optimization is the technical work of making a website machine-readable and answer-ready for large language models: connected JSON-LD entity graphs, llms.txt and sitemaps, and content structured so retrieval systems can extract clean passages.

Does schema markup help with ChatGPT?

Schema alone does not make ChatGPT cite you, but a connected entity graph helps any machine consumer identify what the business is, what it offers, and how the pages relate. It improves interpretation — one necessary condition for retrieval, not a guarantee of it.

llms.txt is a plain-text file at a site's root that lists the site's key pages, primary concepts, and context for language models. It is a courtesy map for LLM tooling — complementary to robots.txt and sitemaps, not a replacement for them.

Home / LLM optimization layer

Technical layer

LLM optimization: schema, llms.txt, and answer-ready content.

Language models do not read pages the way people do. They crawl, parse, and retrieve passages. This page covers the machine-readable layer that makes retrieval possible.

Connected JSON-LD entity graphs

A page that says "we are a company" in prose is invisible to a machine. The same facts as JSON-LD — with stable @id values reused across pages — let any consumer connect the organization, the website, the people, and the products.

{
  "@context": "https://schema.org",
  "@graph": [
    { "@type": "Organization", "@id": "https://example.com/#org", "name": "Example Co" },
    { "@type": "WebSite", "@id": "https://example.com/#website", "publisher": { "@id": "https://example.com/#org" } }
  ]
}

The rule: one canonical @id per entity, the same facts everywhere, and no schema that contradicts the visible page.

llms.txt and the machine-readable files

robots.txt — what may be crawled, at all.
sitemap.xml — the complete list of indexable URLs.
llms.txt — a short, plain-text map of key pages and concepts for LLM tooling.
Consistent metadata — titles, descriptions, and canonicals that agree with the on-page content.

Answer-ready content

Retrieval systems cut pages into passages. Content survives that cut better when it is:

Question-shaped — a heading asks the question, the next sentence answers it.
Self-contained — each paragraph carries its own context instead of pointing at "as mentioned above".
Literal — definitions, numbers, and steps in plain text, not buried in images or PDFs.
Citable — dates, authors, and sources where claims need support.

What LLM optimization cannot do: it cannot inject content into a model, guarantee retrieval, or force citations. It removes the machine-side reasons a system might ignore you. The rest is content quality and time.

Frequently asked questions

What is LLM optimization?: LLM optimization is the technical work of making a website machine-readable and answer-ready for large language models: connected JSON-LD entity graphs, llms.txt and sitemaps, and content structured so retrieval systems can extract clean passages.
Does schema markup help with ChatGPT?: Schema alone does not make ChatGPT cite you, but a connected entity graph helps any machine consumer identify what the business is and how the pages relate. It improves interpretation — one necessary condition for retrieval, not a guarantee.
What is llms.txt?: llms.txt is a plain-text file at a site's root that lists the site's key pages, primary concepts, and context for language models — a courtesy map for LLM tooling, complementary to robots.txt and sitemaps.

What is GEO → the full four-layer picture.