Key takeaways
- Crawler access and clean, fast HTML come before any clever tactic.
- Schema markup helps machines understand who you are and what a page covers.
- llms.txt is a proposed convention with uncertain adoption; treat it as low-cost, low-certainty.
- Write pages that answer specific questions directly, then support the answer.
More people now get answers from AI assistants and answer engines instead of scrolling a list of links. If your business is never mentioned or cited in those answers, you lose visibility you may not see in your usual reports. Generative engine optimization, or GEO, and answer engine optimization, or AEO, are new labels for a practical question: how do we make our content easy for both people and machines to find, understand and trust?
Step 1: Make sure crawlers can reach you
Start with access. Check your robots.txt and any security or bot-protection layer to confirm you are not blocking crawlers you actually want. Different AI systems use different user agents, and some serve both search and model training, so decide deliberately which ones to allow rather than blocking everything by default. Confirm that important content is in the HTML the server returns, since not every crawler runs JavaScript, and keep pages fast and stable.
Step 2: Add structured data where it is true
Schema markup labels the entities on a page in a format machines read easily. Begin with your organization, including name, logo, official profiles and contact details, and add markup that matches real page content: articles with author and date, products with attributes, FAQs where a page truly contains questions and answers, and breadcrumbs for site structure. Validate it with a testing tool. Markup that describes content not visible on the page is worse than none, so keep it honest and in sync with what people can read.
Step 3: Consider llms.txt, with realistic expectations
llms.txt is a proposed convention: a plain text file at the root of your site that points language models toward the pages most useful to them, often with short descriptions. As of this writing, it is a community proposal, and major providers have not committed to relying on it, so you should not expect a measurable lift. It is cheap to create and can help a well-organized site clarify its own priorities. Treat it as an optional experiment after the fundamentals, not as a strategy.
Step 4: Shape content around questions
Answer engines favor pages that resolve a specific question clearly. For each priority topic, lead with a direct answer in the opening lines, then add context, steps, caveats and examples. Use descriptive headings that mirror how people phrase questions, keep paragraphs short and define terms plainly. Include original substance such as your process, criteria and experience, since generic summaries are easy to replicate and rarely cited.
Step 5: Build trust signals
- Show who wrote each piece and their relevant experience.
- Keep dates accurate and update pages when facts change.
- Cite reliable sources for claims that are not your own.
- Make sure your business details are consistent across your site and profiles.
- Earn mentions from reputable, relevant sites, since references elsewhere reinforce who you are.
Step 6: Measure what you can
Attribution here is imperfect. Track referral traffic from known AI assistants where it appears, check regularly what assistants say when asked questions in your field, and note whether your brand and pages are mentioned. Record results on a schedule so you can see trends instead of reacting to single anecdotes.
