LLM SEO: how to optimize for large language models
Understand LLM SEO, retrieval versus training, and practical ways to improve page access, product facts and the evidence behind AI answers.
What is LLM SEO?
LLM SEO is work to make accurate information about your business accessible and useful in answers produced by large language models. Start with buyer questions, readable pages and specific evidence. Distinguish content retrieved during a search from information learned during model training, then track saved answers against a consistent baseline.
Define the job before choosing tactics
LLM SEO means improving the information an AI answer can find and use when someone asks about your category, product or service. The useful outcome is an accurate explanation that helps a buyer decide. Write down the questions where your business belongs, the facts an answer should contain and the pages that establish those facts.
Suppose you sell booking software for small salons. A useful question is “Which booking tools support deposits at two locations?” Your work starts with explaining deposit support, location limits and setup conditions. Repeating “best booking software” across more pages gives the buyer much less to evaluate.
Use the term as a work description. In this guide, it covers access, content, supporting evidence and measurement. Our introduction to generative engine optimization explains the broader GEO concept. For an actual project, specify the questions and answer surfaces you intend to observe.
Separate training from retrieval
A language model learns patterns during training. Anthropic describes pretraining as learning to predict the next word from preceding text. That process creates the model's underlying capabilities before your current question arrives. Anthropic's model glossary.
Retrieval brings outside information into the answering process. A system can search web pages or other records and give relevant material to the model as context. Google Cloud describes this combination of information retrieval and generation as retrieval-augmented generation, or RAG. Google Cloud's RAG explanation.
This distinction changes the practical brief. A retrieval project asks whether the current page is accessible, whether it answers the question and what source links appear in a collected response. A training claim needs evidence about a provider's training process. Keep those as separate questions when evaluating a proposed tactic or supplier's explanation.
Access controls can reflect the distinction too. Anthropic documents separate robots for training collection, search indexing and retrieval initiated by users. Review the purpose of each relevant crawler before deciding which access your publishing policy permits. Anthropic's crawler documentation.
Choose a small set of buyer questions
Collect questions from sales calls, support requests and your existing search-query research. Start with discovery, comparison and requirements. For the salon example, include deposit refunds, multi-location scheduling and migration from an existing calendar. Give each question a reason for inclusion and a page that should help answer it.
Keep branded questions in their own group. “Does this product support deposits?” tests an explanation after the product has been named. “Which tools support deposits?” tests whether it enters the answer unaided. Use both groups when they reflect customer needs, and keep their results separate.
Choose ten questions for an initial editorial review. Read every collected answer and record the business decision it could affect. If several questions expose the same missing explanation, combine the resulting work into one page edit. Preserve distinct questions in the measurement sheet so their later answers remain comparable.
Make the right pages accessible
Open the target page while signed out. Read its main content, follow its internal links and check that the intended destination loads. Give the website maintainer concrete access problems to investigate, including the affected URL and the conditions under which the problem appeared.
For Google Search's AI features, Google's guidance requires an indexed page eligible for a snippet and participation in Search generative AI features. It also says special AI markup is unnecessary. Use those documented requirements when checking that particular surface. Google's generative AI optimization guide.
You can use our free llms.txt generator to create llms.txt, an optional, experimental file that is not a Google ranking factor.
Build a short page inventory rather than starting with a site-wide rewrite. Include the main product page, relevant documentation, pricing conditions and one useful comparison. Record who maintains each page. An access issue and an outdated capability claim need different owners, even when they affect the same buyer question.
Put the answer beside its conditions
Write a direct explanation under a heading that matches the reader's task. Follow it with the conditions that change the answer. For deposits, explain when payment is collected, how refunds work and what a business must configure. Have the responsible product role approve the facts.
Use examples to make broad claims testable. A worked booking scenario can show the deposit amount, cancellation point and resulting refund. Label invented numbers as examples. If a feature differs by country or plan, state that beside the feature rather than leaving the reader to discover it elsewhere.
Choose the format for the decision. A comparison table helps when several products share the same evaluation criteria. Numbered steps help with setup. A short paragraph can answer a simple eligibility question. Maintain one complete explanation for each need, with links to deeper instructions where the reader needs them.
Build evidence that someone can check
For every material claim, identify its supporting record. That might be current product documentation, a published methodology or a dated test you actually performed. Explain how a result was obtained and which conditions applied. Replace “works for everyone” with the supported use cases and exceptions.
Review the same fact across your public pages. If the pricing page says one location and the product guide says several, resolve the inconsistency before requesting more mentions elsewhere. Keep an internal list of approved facts and the pages that contain them so future changes have a clear starting point.
Inspect third-party pages returned in your answer samples. If a directory repeats an obsolete feature limit, follow its correction process with a link to the current documentation. Offer verifiable information and let the publisher make its editorial decision. Record the request and the eventual page change separately.
Connect every edit to a saved baseline
Before publishing, save the exact question, full answer, collection date and interface settings. Record brand mentions, the order of relevant brands and returned source URLs in separate fields. Attach the page problem you found. This lets a reviewer follow the path from observation to proposed work.
After editing, save the previous text, replacement text and publication date. Choose a review date and repeat the original question under the same recorded conditions. Keep a log of failed collections and retries. Add new questions to a separate group so the original comparison retains its meaning.
Use the result to choose the next action. An answer with an old feature limit points back to the relevant evidence. A correct explanation that omits your brand may prompt a review of fit and competing products. A changed source list gives you new pages to inspect. Write the specific follow-up beside the saved answer.
Write a brief someone can implement
Turn the finding into a small, reviewable assignment. Name the page, the buyer question, the missing fact and the approved reference. For the booking example, the brief might ask for a refund scenario beside the deposit explanation, checked against the current policy. Specify who confirms the facts and who publishes the change.
Finish with an acceptance check: the explanation is visible, its conditions match the policy and the setup link reaches the correct instructions. Add the publication date and next observation date after those checks pass.
Set a clear boundary around your control
You control your pages, their access settings and the evidence you publish. Providers control training selection, retrieval, answer assembly and updates; publishers control their own coverage. Inclusion is not guaranteed. Answer samples describe their recorded questions and conditions, and before-and-after differences do not prove that an edit caused a change. Judge the work by verified improvements and observed results together.
SearchSeal monitors AI Overview, ChatGPT, Gemini and Perplexity today, saving answers to tracked questions with brand mentions, position, rivals and returned source links. Optional, read-only Google Search Console and Bing Webmaster connections add page demand to help rank fixes. Start by finding who you're compared with, using competitor suggestions to prepare your question set.
Frequently asked questions
Where should I begin with LLM SEO?
Choose a buyer question, save the answer you observe and identify the page that should establish the relevant facts. Check that page for access problems, missing conditions and outdated information before planning broader content work.
Which pages should I review first?
Start with pages tied to actual buying decisions: the product explanation, relevant setup documentation, pricing conditions and useful comparisons. Prioritize a specific factual or access problem over a general rewrite.
Should every question get its own page?
Group questions that share one reader need into a complete explanation. Create a separate page when the task or audience requires one, and link between related explanations where the reader needs more detail.
What should I record after an edit?
Keep the old and new text, publication date, question wording and later saved answers together. Add the collection conditions, any failures and the next specific issue you intend to investigate.