How does an LLM process content?

A traditional search engine ranks and lists. An LLM answers, and it draws from sources to build that answer. It works through two channels: first, its training data, which was used to train the model originally, and second, real-time retrieval, often called RAG (Retrieval-Augmented Generation). This is the kind of approach used by tools such as Perplexity or ChatGPT browsing and search modes.

If you are present in both channels, meaning your content is represented in training data and fresh crawlers can also find it, there is a better chance you will be cited. If you are absent from training data and your site is not structured in a way crawlers can digest, you are left with classic search results. That is becoming a smaller slice of discovery.

Structure: LLMs do not want to guess

LLMs work much better with well-structured content that has a clear hierarchy. At first glance, this sounds identical to classic SEO advice, and for the most part it is. But there are a few differences.

Your heading structure should be consistent: H1 for the title, H2 for main sections, H3 for subsections. Do not use headings for decoration, and do not make the whole page a single unstructured stream of text. The point is that AI should be able to pull out a paragraph from its surrounding context and still have it make sense on its own.

Lists, comparison tables, and numbered steps are especially useful for LLMs. They are easy-to-process, clear data points that can be cited more confidently in an answer.

llms.txt: still underused, but worth considering

There is a relatively new and increasingly discussed proposal: llms.txt. Think of it as an AI-friendly content map. It is a simple Markdown file placed at the root of your website, summarising what the site is about, which pages matter most, and where key documents can be found.

In effect, you give the LLM a map instead of making it infer the structure from raw HTML. The format is minimal: an H1 title, a short summary, then links with short descriptions.

If you use WordPress, Yoast SEO already supports automatic generation. Elsewhere, it usually has to be created manually, but for many websites this is at most one or two hours of work.

Schema markup: make content machine-readable too

JSON-LD based Schema.org structured data becomes especially valuable in LLM optimisation. Schema tells AI that this is a blog post, this is an FAQ page, this is a company profile. It provides context that the model would otherwise have to infer.

What is especially useful for LLMs: author information, dates, update dates, and entity relationships. Who wrote the article? When was it published? When was it updated? Which organisation owns the content? These all influence how much trust a model may place in a source.

The minimum worth implementing: Article or BlogPosting schema for articles, Organization schema for company pages, and FAQ schema for frequently asked questions.

Credibility: E-E-A-T logic in an AI context

Google's E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) also applies to LLMs, just in a different form. When AI decides which source to draw from, it considers whether the content is consistent with other reliable sources and whether identifiable expertise stands behind it.

Author profiles, citations, and appearances in professional publications all strengthen AI visibility. Not because an LLM measures backlinks exactly like a classic search engine, but because you appear as a trustworthy entity across training data and real-time sources.

Content updates are not optional

RAG-based systems pay attention to when content was last updated. An article written two years ago and never maintained is less likely to appear in a fresh AI answer than a recently updated source.

This does not mean everything needs to be rewritten every month. But key pages are worth reviewing quarterly: are there outdated facts, broken links, or missing points? The dateModified field in schema markup signals this to crawlers.

Technical foundations

A few things matter even when the content itself is excellent:

  • robots.txt: Check that you are not accidentally blocking AI crawlers. For OpenAI, OAI-SearchBot is relevant for visibility in ChatGPT search, while GPTBot relates to model training. Google uses Google-Extended as a separate robots.txt token, and Anthropic separates bots for training, search indexing, and user-directed retrieval.
  • JavaScript rendering: Many AI crawlers execute JavaScript only partially or not at all. If your site is heavily JS-based (React, Next.js SPA), server-side rendering or static export is safer than relying only on client-side rendering.
  • Sitemap: Keep your sitemap.xml up to date. It is especially important for systems that rely on search indexes and crawler data.
  • Meta description: Do not leave it empty. For AI crawlers, this is an early, high-signal summary of the page; a well-written meta description can influence whether the model looks deeper into the content.

The point

Traditional SEO is not dying. But a new layer is arriving beside it, increasingly called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization).

The good news is that the two areas overlap heavily: what is good for Google is usually good for LLMs too. The extra steps, such as llms.txt, refined schema, and a regular update strategy, are not huge investments, but they create a meaningful advantage over competitors who have not thought about this yet.

So the question is not SEO or LLM SEO. The question is whether your content is understandable, structured, credible, and machine-readable. If it is not, you can easily become invisible in the AI era.