5 content types for LLM visibility: Part 3

So your SaaS tool shows up in ChatGPT and other AI search tools.

This issue builds on parts 1 and 2 of my LLM optimization series — where I shared a checklist for LLM visibility and how to train ChatGPT on your product.

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Last week, in Part 2 of this series on optimizing your product-led content library for LLMs, we covered two core principles that explain how tools like ChatGPT learn: pattern generalization and repetition.

Now for the next step: what content types help you train LLMs using those very principles?

I’ve got five for you.

Important context before we begin, some content is far more surfaceable in AI tools than others. Simply because:

• It’s structurally scannable, broken into clear, standalone sections

• Aligned with the prompts your buyers are already asking AI

Making it easier for LLMs to parse through, summarize, and surface your content for buyers.

Now for the 5 content types to build your SaaS tool’s LLM visibility:

1. Category explainers and listicles

Folks still fresh in their buyer journey typically search “what is X type of tool.”

For instance, “What is an email deliverability software?” and “X best email deliverability software.”

Not only does this type of content help you gain LLM visibility, but it also builds your topical authority.

Meaning: it improves the odds of your product’s name showing up when buyers prompt for tools in your category.

2. Comparative content

Comparative content matches with prompts like “best alternatives to [X]” or “[Tool A] vs [Tool B],” helping you show up in front of ready-to-buy folks.

Start by talking to sales to find out the most pressing competing tools buyers are comparing you against.

Then create well-structured and objective comparative content.

3. Use case content

Use case content bridges the gap between your buyers’ problem and the solution you offer.

In doing so, it helps LLMs match your tool to the problems potential buyers search for.

To start planning this content, list your buyers’ pain points. Then, in a column next to it, specify product features that solve it.

From there, create content like this:

How to solve [specific pain point] without [undesirable old method]

Example:

• How to reduce support ticket volume with an AI-powered help center.

• How to reduce support ticket volume without hiring more agents.

4. How-to guides

Where use case content ties your product to buyers’ pain points, jobs-to-be-done, and outcomes, how-to content walks readers on how to solve a specific problem.

You’ll want to lay out step-by-step tutorials to solve the specific problem — include a showdown of the entire workflow (also explaining how to use other tools involved in the process, if any).

Example: How to reduce onboarding drop-off with contextual product tours.

Remember, this content type matches with prompts like “what’s the best to …” and “how do I…,” making it ideal for decision-stage queries that interested buyers ask LLMs.

5. Frequently asked questions

These naturally align with Q&A prompt formats — especially for AI tools like ChatGPT and Perplexity.

Each question-answer pair can be surfaced as a standalone answer, making them ideal for snippet-style LLM responses.

Keep in mind, for these to surface in LLMs, give direct, to the point answers. Use steps from this checklist to plan and write FAQs (and the rest of the content types we’ve covered above).

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Hope this sparked a few ideas.

I’ll be back next week with more on what actually goes into building a product-led library that shows your content in search and LLMs.

Until then, have a good one!