How to get your course or community recommended by AI assistants

There are two routes. Some knowledge comes from training data: pages the model read before its cutoff. Increasingly, assistants also search the web at the moment you ask, read a handful of pages, and summarize them with links. Perplexity works this way by default, and ChatGPT, Gemini and Claude all do it for many questions.

The second route is the one you can influence this month. When an assistant searches, it favors pages that answer the question directly, that agree with other sources, and that look like a real, specific offer rather than generic marketing. Your job is to be the clearest, most consistent answer to the questions your future members ask.

Every course or community needs one public page that a stranger, or a crawler, can read without signing in. It should answer, in plain sentences: who it is for, what problem it solves, what is inside (courses, live calls, community), how much it costs, and how to join.

Write the first two sentences as if they will be quoted, because they will be. "The Freelance Designers Guild is a paid community for independent brand designers who want steadier clients. Members get a pricing course, weekly portfolio reviews and a job board for $29 a month." That is a sentence an assistant can lift whole. "Welcome to your journey" is not.

Make a list of ten questions a prospective member might ask an assistant: "best community for learning watercolor", "is X worth it", "X vs Y", "how much does X cost". Then make sure each one is answered somewhere public, in your own words, with a heading that matches the question.

A short FAQ on your landing page covers most of these. Answer honestly, including who your community is not for. Assistants are increasingly good at noticing pages that only say positive things, and a candid "not a good fit if you want one-to-one coaching" makes the rest of the page more believable.

Assistants cross-check. If your landing page says $29 a month, your social bio says $25, and an old podcast page says it is free, the assistant either picks one at random or leaves you out because it cannot tell which is true. Pick one name, one one-sentence description, one price and one link, and use them on your landing page, social bios, directory listings and guest appearances.

Audit this once a quarter. Search your community's name, open the first ten results, and fix any that are out of date. This is boring, and it is probably the highest-value hour on this list.

A recommendation means more when it does not come from you. Ask happy members to leave a review where reviews are visible, mention your community when they answer questions in public forums, or describe it on their own blog or newsletter. Never script or pay for reviews; ask people to say what was true for them.

Guest appearances work the same way. A podcast episode page or a newsletter feature that says "Jane runs a community for X" is exactly the kind of third-party confirmation an assistant uses to decide you are real and relevant.

llms.txt is a proposed convention: a plain Markdown file at the root of your website (yoursite.com/llms.txt) that tells AI tools, in a few lines, what the site is and which pages matter most. It is not a ranking guarantee and not every assistant reads it, but it is cheap to write and it forces you to state your offer clearly in one paragraph, which helps everywhere else.

A useful llms.txt has a one-line title, a two- or three-sentence summary with your price and audience, and a short list of links to your landing page, pricing, FAQ and best free content. Keep it as current as your landing page.

Assistants recommend sources as well as products. A free, genuinely useful guide on a narrow question in your field ("how to price a brand identity project") gets read, cited and linked, and it carries your community's name with it. One excellent guide beats ten thin posts.

Once a month, ask three assistants the same five questions your ideal member would ask. Note whether you appear, what they say, and which pages they cite. When an assistant gets a fact wrong, the cited page usually tells you where the wrong fact lives. Fix it there.

The rule to keep: if an assistant cannot read it, it cannot recommend it, and if two of your pages disagree, it will not trust either.

HiveLearn gives each community a public landing page you control, and public community pages are served as readable HTML to search crawlers, for communities that are public and have real content. That matters because many community apps render everything with JavaScript behind a login, which leaves crawlers and assistants with an empty page.

Public communities can also appear in HiveLearn's Community Discovery directory at hivelearn.app/discover, which is one more consistent, third-party listing of your name and description. Course ratings and reviews come from your learners, and every certificate has a public verification page (hivelearn.app/verify/<code>) that a graduate can link from a profile, which is a small but real mention of your program on the open web.

HiveLearn practices what this guide describes: it publishes its own llms.txt and answer-first guides like this one. The steps above work on any platform, though; the platform only decides whether your public pages are readable in the first place.