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SEO for large language models
LLM SEO
LLM SEO is a shorthand for making a website usable by large language models, both those that search the web live and those trained on web data.
Most of it is good technical SEO and editorial discipline; a small part is specific to how models read pages.
Know which technical and content changes help language models use your site, and which popular claims lack evidence.
In the paper's benchmark, adding citations, quotations from relevant sources and statistics were among the methods that most improved source visibility in generated answers. arXiv: GEO: Generative Engine Optimization (Aggarwal et al.) ↗
llms.txt is a proposed, community-input convention for a Markdown file that helps AI agents use a website. llmstxt.org: the /llms.txt proposal ↗
New to the topic? Read the background on artificial intelligence.
SEO for large language models
what is LLM SEO?
Making your site fetchable, readable and consistent for language models.
Live-search assistants fetch pages at answer time; training crawlers collect pages for future models.
LLM SEO covers access for both, clean extractable text and consistent facts about your organisation across pages.
Access for crawlers.
Clean extractable text.
Consistent entity facts.
SEO for large language models
should we allow AI crawlers?
Decide per crawler: search crawlers affect citation, training crawlers affect future models.
OpenAI documents separate user agents for search and training, so site owners can allow one and block the other.
Blocking a search crawler removes the chance of being cited by that assistant's live answers.
Check your robots rules and any CDN bot protection, which can block crawlers without anyone noticing.
OpenAI's crawler documentation and Perplexity's both separate search crawlers from user-triggered fetches, which is central to LLM SEO decisions.
SEO for large language models
does llms.txt help?
It is a low-cost proposal; no major provider has said it changes answers.
The llms.txt convention suggests a Markdown file listing a site's key pages for language models.
predeploy publishes one because it is cheap, but we do not claim it affects citation, and nor should any agency without provider evidence.
The llms.txt proposal itself says it is open for community input, so treat it as optional LLM SEO hygiene.
| Check | What to establish |
|---|---|
| 1 | Cheap to publish. |
| 2 | No provider confirmation. |
| 3 | Do not rely on it. |
SEO for large language models
what content changes help language models?
Direct answers, specific sourced facts and stable entity details.
Models quote passages, so each passage should make sense on its own.
Specific, attributed figures were among the changes the original GEO research found helpful.
Keep your company name, services, location and people consistent across pages and structured data.
The GEO paper gives LLM SEO its best evidence that sourced statistics and quotations help.
SEO for large language models
how does LLM SEO fit with GEO?
It is the technical half of [generative engine optimisation](/generative-engine-optimisation).
GEO combines LLM SEO's access and format work with answer-first writing and measurement.
A GEO audit checks both halves, and a GEO agency implements them.
LLM SEO: technical half.
AEO: writing half.
GEO: both plus measurement.
SEO for large language models
how do we check whether language models can read our pages?
Fetch them as a crawler would and read the text that comes back.
Request the page without JavaScript and check that the main content, headings and answers are present in the returned HTML.
Check the server logs or CDN analytics for AI user agents to see whether they are visiting and what responses they receive.
Ask a few assistants with web access about a fact that appears only on that page, and note whether they find it.
These checks form part of a GEO audit, recorded with dates so they can be repeated.
These checks make LLM SEO measurable rather than speculative.
Fetch without JavaScript.
Check logs for AI user agents.
Test a page-unique fact.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to llm seo and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing llm seo.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai powered gtm service or read the geo and ai search visibility overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
In the paper's benchmark, adding citations, quotations from relevant sources and statistics were among the methods that most improved source visibility in generated answers. arXiv: GEO: Generative Engine Optimization (Aggarwal et al.) ↗
llms.txt is a proposed, community-input convention for a Markdown file that helps AI agents use a website. llmstxt.org: the /llms.txt proposal ↗
OpenAI documents OAI-SearchBot for ChatGPT search and GPTBot for training, and states each robots.txt setting is independent. OpenAI: overview of OpenAI crawlers ↗
Perplexity documents PerplexityBot for surfacing and linking sites in search results, separate from user-triggered Perplexity-User fetches. Perplexity: Perplexity crawlers ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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