LLM-structured content, schema and semantic SEO

LLM-structured content is content arranged so a large language model can pick out the facts without guessing. That means descriptive headings, a direct answer near the top, one idea per paragraph, and lists or tables where the information is naturally a list or a comparison.

It also means the important facts exist as text in the page. Facts that appear only in images, videos or content loaded by scripts may not be read by every crawler.

Schema markup is structured data, usually written in a format called JSON-LD using the schema.org vocabulary, that states facts about a page in a form machines can parse. It tells a system that a page describes an organization, a service, a product or a set of questions and answers.

Structured data helps machines interpret a page, and search engines document how they use it. How much each AI assistant relies on it when writing answers is not published and likely varies, so it is best treated as a way to remove ambiguity, not as a guarantee of being named.

llms.txt is a proposed convention: a plain text file at the root of a website that gives language models a short, curated guide to the site. It is a proposal, not an adopted standard, and AI assistants are not known to obey it.

Adding one is low risk and can be a tidy summary of your key pages, but it should not be expected to change AI answers on its own. Clear pages and accurate structured data are the more dependable work.

Semantic SEO is organizing a site around topics and the entities within them, such as your company, products, services and locations, instead of around isolated keywords. Each entity is named consistently, described once in a clear place and linked from related pages.

This helps search engines and language models alike. Both try to work out what things a page is about and how they relate.

Structural work is checked in two ways. First, markup can be validated with standard structured data testing tools. Second, Kraffic re-runs your tracked prompts on Claude, ChatGPT and Google Gemini and shows whether visibility, cited sources or fact-check findings changed. Because AI answers vary between runs, the trend across scans is what to read.