Recommending the right content to each member with AI

Every mature membership has the same hidden problem. The library keeps growing, and members see less of it. New members feel overwhelmed by hundreds of lessons, long-time members assume they have seen everything, and your best resources sit unopened because nobody knows they exist. Members who cannot find what they need eventually decide the membership is not worth the money.

Recommendations fix this by answering one question for each member: what should I look at next? Streaming services use complex algorithms for this. You do not need anything so elaborate. With AI, a small membership can build a clear, reliable recommendation system out of three things: a well-tagged library, a map of sensible next steps and short notes explaining why each suggestion fits.

Why members cannot find what they need

Content libraries are usually organized the way the owner created them, by date or by category, rather than the way members think. A member rarely thinks “I would like something from the Techniques category.” They think “my borders look messy and I have twenty minutes.” Good recommendations bridge that gap by matching content to a member's problem, level and available time.

If you have not yet thought through which problems your content solves, start with this guide to building your content around your members' biggest problems. It will make every step below easier.

Step one: tag your library with AI

Recommendations are only as good as the information about each piece of content. Most libraries have titles and little else. AI can help you add consistent tags quickly.

Export a list of your content with titles and a short description or the first paragraph of each. Then ask an assistant such as ChatGPT, Claude, Gemini or Microsoft Copilot to tag it:

Here is a list of content from my membership site about [your topic], with a title and short description for each: [paste list]. For each item, add these tags: Level (beginner, intermediate or advanced), Format (video, article, worksheet, recording or course), Time needed (under 15 minutes, 15 to 45 minutes, or longer), Problem solved (one short phrase in the member's own words) and Good next step (the title of one other item from this list that naturally follows). Return a table. If you cannot tell a tag from the description, write UNSURE rather than guessing.

The instruction to write UNSURE matters. Without it, AI tends to fill every cell with a confident guess. Review the table yourself, correct the levels and fill in the unsure cells. You know your content; the AI is only saving you typing.

Step two: map the next steps

With tags in place, you can build simple paths. Picture a hypothetical garden design membership called Plot and Path. Its owner, Esther, used her tagged table to ask the AI for starter paths for three member goals: planning a first border, redesigning a small urban yard, and planting for wildlife. The AI proposed an ordered list of five to seven items for each, drawn only from her library.

She then edited each path by hand, swapping two items that assumed skills beginners would not have. The result was a set of paths she could trust, not a black box she could not explain.

You can also ask the AI to find the gaps. If it cannot build a sensible path for a common goal without an item you do not have, that is a content idea worth noting.

Step three: write the “why this is for you” notes

A recommendation with a reason is far more persuasive than a bare link. For each key item, ask the AI to write a one or two sentence note aimed at the member who would benefit:

• “If your borders look busy rather than full, this 12-minute video shows how to repeat three plants so the eye can rest.”
• “Finished the planning worksheet? This walkthrough shows how to turn your sketch into a shopping list.”

Edit these into your voice and check that each one accurately describes the content. A note that overpromises teaches members to ignore your suggestions.

Step four: deliver recommendations where members look

Once you have paths and notes, put them where members already are:

• At the end of each lesson: a “next, try this” link from your Good next step tag.
• On the member home page: a short list for each membership level or goal, if your platform can show different content to different groups.
• In email: a weekly or monthly note that suggests one item based on the member's chosen goal.
• In support replies: a saved link to the right path when a member asks where to start.

Notice that none of this requires feeding members' personal data into an AI tool. The AI works on your content; your platform or email tool uses the tags and groups you already have to decide who sees what. If you later want recommendations based on individual activity, do that inside your platform or with a vendor whose business terms you have read, and review how to protect member privacy when you use AI tools first.

Review and improve over time

Before anything goes live, click through every path as a member would. Check that each link works, each level is right and the order makes sense. Then keep an eye on what happens. If an item is recommended often but rarely finished, the note may oversell it or the item may need refreshing. If members keep asking where to find something, add it to a path.

Every few months, re-export new content and run the tagging prompt on just the additions so your library stays consistent. This approach builds naturally on the personalization basics of member situations and simple variations.

Your first steps

1. Export your content titles and short descriptions.
2. Run the tagging prompt, then review and correct every row.
3. Pick your three most common member goals and build a path for each.
4. Write a short, honest note for every item on those paths.
5. Add next-step links to the end of your most popular lessons.
6. Click through each path yourself before sharing it.
7. Note any gaps the AI found and add them to your content plan.