Finding gaps in your content library with AI

After a year or two of publishing, most membership libraries are large enough that the owner can't hold the whole thing in their head. You know the pieces you're proud of. You're less sure what's missing. Meanwhile members keep asking questions in support emails and forums, and some of those questions point to content you've never made, while others point to content you have made that members simply can't find.

A gap analysis compares two lists: what members need and what your library covers. Doing it by hand means reading hundreds of titles against hundreds of questions, which is why it rarely happens. AI can do the matching quickly and give you a clear picture of where the library is thin. Deciding which gaps matter is still your job.

Gather the two lists

You need a content list and a questions list, both as plain text.

The content list is every piece in your library with its title and a one-line description. If your descriptions are missing or vague, it's worth asking AI to draft them in batches from the content itself and checking them as you go; better descriptions will help members find things too.

The questions list comes from anywhere members ask for help:

• Support emails and help desk tickets.
• Forum and community questions.
• Questions asked in live sessions.
• Survey responses and cancellation reasons.
• Searches members run on your site, if your platform records them.

Strip out names, email addresses and anything that identifies a member before this list goes near an AI tool. You want the question, not the person. If the material is sensitive, use a business plan whose data and training settings you've checked. Writing FAQs from your support inbox with AI covers ways to gather and clean support questions.

Ask AI to map questions to content

Nadia runs a membership for wedding photographers, with around three hundred articles, videos and templates. She exported her content list, gathered a few months of anonymized questions and gave both to an assistant with this prompt:

I run a membership for [describe your members]. Below are two lists. List A is my content library, with titles and descriptions. List B is questions members have asked, with personal details removed. For each question in List B, say whether it is fully covered, partly covered or not covered by List A, and name the pieces that cover it. Then group the not-covered and partly covered questions into themes, and for each theme say how many questions fall into it. Only use the descriptions I've given; if you're unsure whether a piece covers a question, say so. List A: [paste content list]. List B: [paste questions].

For large libraries, tools that work with uploaded documents make this easier. NotebookLM, ChatGPT, Claude, Gemini and Microsoft Copilot can all work with files, though how much they can handle at once varies and tools change. Asking AI questions about your own documents explains how to cross-check answers against the source.

Separate content gaps from findability gaps

The results usually show two different problems. Nadia's analysis found themes where she had nothing, such as pricing packages for elopements. But it also found questions about second-shooter contracts that were fully covered by an existing template. Members just hadn't found it.

That second group is a findability gap, and it needs a different fix: a clearer title, a better description, a link from the related lesson, or a mention in your welcome sequence. Ask the tool to highlight these:

From your analysis, list the questions that are fully covered by existing content but still get asked. For each, suggest why members might not be finding the answer, based only on the titles and descriptions, and suggest a clearer title or description.

Findability fixes are often quicker wins than new content, and they make the library you already have more valuable.

Check the matching yourself

AI matching is based on the words in titles and descriptions, not on a deep reading of every piece. It will get some matches wrong. Before acting, spot-check the results:

• Open a few pieces marked as covering a question and confirm they actually answer it.
• Look at several not-covered questions and check you really don't have something relevant.
• Question any theme that seems surprisingly large or small.

Nadia found two pieces the tool had matched on a shared keyword that were about different topics entirely. A quick check caught them before she made decisions based on the result.

Decide which gaps to fill

Not every gap deserves new content. For each theme, ask yourself:

• How often does it come up?
• Is it central to the promise of your membership?
• Are you the right person to answer it, or would a guest expert be better?
• Is it better answered with a short help article, a full lesson, a template or a live session?

Some gaps are deliberate. If members keep asking about something outside your scope, the right response might be a short note pointing them elsewhere. For a wider way of thinking about what your content should cover, see building your content around your members' biggest problems.

Add the gaps you choose to fill to your idea list. Brainstorming a year of content ideas with AI shows how to filter and prioritize ideas once you have them.

Make gap analysis a regular habit

Libraries change and so do members' questions. Running this analysis every six months or so keeps your content aligned with what members actually need. Keep your content list and questions list updated between rounds so the next analysis takes an hour rather than a week.

Your first gap analysis

1. Export or build a content list with clear one-line descriptions.
2. Gather a few months of member questions and remove all personal details.
3. Run the mapping prompt and review the themes.
4. Separate content gaps from findability gaps and fix the quick wins first.
5. Spot-check the matches before you act.
6. Choose which gaps to fill, and in what format.