When a buyer asks ChatGPT which tool to use, the model composes its answer from sources it trusts: comparison pages, category directories, reddit threads, review roundups. LLM SEO is the work of being present in those sources. It looks like classic SEO from a distance, but the ranking surface is different and so is the playbook.
What models actually cite
Run the same buying questions through the big assistants and patterns repeat:
- Listicles and roundups dominate. The page called best tools for X gets quoted, whoever wrote it.
- Comparison and alternatives pages get quoted for head-to-head questions and almost nobody builds them properly.
- Reddit threads appear constantly, because models treat them as human consensus.
- Category directories feed the long tail. A verified listing is a machine-readable fact about what your product is.
The playbook, in order of leverage
- Get onto the lists that already get cited. Find the roundups models quote for your category and pitch the ones you are missing from. This single lane moves share of voice fastest.
- Build the comparison pages yourself. A page comparing you honestly with each named rival gives the model exactly the shape it wants to quote for versus questions.
- Be present in the ranking reddit threads. Covered fully in our reddit strategy guide.
- Cover the directories. We keep a free verified list of 110 AI and startup directories for this.
Measuring it
Share of voice: ask a fixed panel of buying questions across several models on a schedule and count who gets named. The same questions every month, so movement means the market changed, not the questionnaire. That is the number this whole discipline exists to move and it is measurable for any brand today.
The order matters because sources compound: a directory listing feeds a listicle, the listicle gets cited, the citation trains the next model. Every layer you skip weakens the ones above it.
Related reading
What AI visibility means and how to score yours, and the broader discipline some call generative engine optimization.
found does this work as a product.
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