Imagine a small language school looking for a booking tool. One product page explains class capacity, recurring lessons, payment handling and cancellation rules, with a short example of a teacher setting up a weekly class. A reader can quickly connect the product to the school's day-to-day work.
This hypothetical example offers a practical starting point for AI visibility, often called generative engine optimization (GEO): explain a real product in language that answers a real question. Technical access, a clear identity and supporting evidence help that explanation become useful source material. A consistent observation method then shows how the content appears in actual answers.
Scope of this guide
Sitelemetry's AI visibility area scores readiness from the static HTML of public pages, robots.txt, structured data and /llms.txt. It does not query ChatGPT, Claude, Perplexity or Google and does not track mentions or citations in AI answers. No change to a site obliges an assistant to cite it.
Understand the question behind the question
Customer questions reveal the context in which a product becomes useful. Someone exploring booking tools may want to know how siblings share a payment, how teachers set their availability or how cancellations work. Begin with questions from sales conversations, support requests and product onboarding.
Choose one audience and a specific decision. Write the question in the words that audience uses, including language, location or practical conditions when relevant. A small school and a national training company may benefit from different explanations. Give each page a distinct purpose and enough detail for its intended reader to take the next step.
See how a page becomes source material
Assistants and search products find information in different ways. A search-enabled answer can retrieve public pages and combine information from several sources. Technical access helps a page become available; relevant content helps answer the question; a citation records its use in a particular response. Understanding these stages helps you choose what to improve and what to observe.
Build on useful internal links, indexable public pages and readable important text. Pair visual explanations with meaningful text and make key content accessible as the page renders. Google's JavaScript guidance explains initial HTML and rendered content. Its AI-features guidance points website owners toward sound search and content foundations for Google's AI search experiences. Google's newer guide to optimizing for generative AI features adds that you do not need new machine-readable files, AI text files or special markup to appear in Google Search. What an optional llms.txt file can and cannot do is covered in the llms.txt guide.
Make the identity easy to follow
Use a recognizable organization and product name across the title, main heading, introduction, about page and contact details. Explain the relationship between translated names and abbreviations. This gives readers a clear path from the name they recognize to the business and product being described.
Structured data expresses facts in a machine-readable vocabulary. Schema.org's Organization type, for example, includes names, alternate names and URLs. Match those fields to the visible business. Documented awards, related brands and supported review information can contribute to a coherent identity. Start with a complete human explanation, then express those same facts in structured form.
Build an answer people can use
Lead with the direct answer, then explain how it works, who it suits and what the reader should do next. For the school example, a section about recurring lessons could describe how a weekly slot is created, what happens on a holiday and how a learner changes their booking. A short worked example turns an abstract feature into a process someone can evaluate.
| Customer question | Useful content | Evidence to include |
|---|---|---|
| Will it fit our workflow? | A realistic sequence of steps | Documented behavior and relevant screenshots |
| What does the price cover? | Clear inclusions and billing terms | Current public pricing and conditions |
| How does it differ? | A comparison on relevant criteria | Sources and review date |
| What supports the claim? | Method, assumptions and result | Original evidence or a directly relevant source |
Use headings that describe the content, descriptive links and concise explanations of specialist terms. Use the format to help someone follow the reasoning and find the detail they need.
Support claims and keep them current
Distinguish facts, examples and opinions so the reader understands the role of each. Label a hypothetical scenario as an example. For a measured result, explain what was measured and under which conditions. In a comparison, link directly to the source that documents the relevant feature so a reader can explore it in context.
Include a genuine review date for changing subjects such as pricing, supported integrations or regulatory requirements. Review the content when those facts change. Google's helpful-content guidance emphasizes useful, reliable material for people. Original examples, clear authorship and an explained method help readers understand and assess a page.
Observe answers and visits with a repeatable method
Keep a small question set drawn from customer needs. Record the provider, question, language, date and relevant context when checking an answer. Note which sources were cited and how the answer describes your product. Repeat the same method over time to build a series of observations and identify recurring patterns.
Track referral visits separately in analytics and study what visitors do next. Read these signals together: content quality describes what you improved, recorded answers show observed visibility and visits show subsequent activity. Individual examples add context to the numbers. Use a suitable observation period and compare similar question sets and time windows as the evidence grows.
Put the lesson into practice
Sitelemetry's AI visibility audit, part of the paid plans from $49 a month, scores readiness from the static HTML of a sample of your pages, robots.txt, JSON-LD structured data and whether /llms.txt is present. It does not query ChatGPT, Claude, Perplexity or Google and does not track mentions or citations in AI answers, so keep the question set and answer log described above.
Use its findings to select a specific passage to improve, then revisit your question set and referral evidence. The result is a repeatable editorial process grounded in customer questions and observable outcomes.
Common questions
Where can a newly established website begin?
Choose one frequent customer question with a clear business purpose. Publish a complete, accurate answer with a practical example and useful links before expanding into adjacent topics.
When is an llms.txt file useful?
llms.txt is an informal proposal for a Markdown file that points AI tools to a site's key pages. Google Search says it ignores these files, so publishing one neither helps nor harms visibility there. It can still help tools that choose to read it, so treat it as an optional documentation aid and put readable pages and accurate content first.
How do I know whether visibility is improving?
Compare recorded answers and citations using a consistent question set and method. Examine referral visits and customer actions alongside them, using comparable periods and noting relevant changes in the provider or audience.
Sources & further reading
- Google: AI features and your websitedevelopers.google.com
- Schema.org: Organizationschema.org
- Google: JavaScript SEO basicsdevelopers.google.com
- Google: helpful and reliable contentdevelopers.google.com
- Google: optimizing for generative AI features in Searchdevelopers.google.com
Prepared by the Sitelemetry editorial team. Explore the linked sources for further detail.
