Internal linking has always been an SEO staple: it helps crawlers find pages and spreads authority through a site. In Generative Engine Optimization it does something more specific and more valuable. AI models do not just index your pages, they try to understand how your brand, products, and concepts relate to one another. Your internal links are the map they read to build that picture.
When the links are weak, generic, or contradictory, the model cannot form a coherent view of your brand, and coherence is exactly what earns a confident recommendation over a competitor. This guide gives you a linking strategy built for how AI reads a site: descriptive anchors, a hub-and-spoke structure, and consistent entity language, with a way to check whether it is actually working.
Key takeaways
- Anchor text is a semantic signal, not a keyword slot. Write anchors that summarize the destination page's main entity so the model knows what it points to before crawling it. - Organize content as hubs and spokes: a broad pillar page linking to specific sub-topics, and sub-topics linking back and to each other, so the model reads a clear topic hierarchy. - Consistency is the lever. Refer to each entity, product, or concept with the same wording across links so the model builds one clean knowledge graph instead of several fuzzy ones. - Link related concepts to each other, not just up and down the hierarchy, because the relationships between your ideas are what the model reasons over. - Internal linking supports understanding, but citations and entity clarity still decide visibility, so treat it as one layer in a larger GEO strategy.
Step 1: Write descriptive, semantic anchor text
Classic SEO warned against over-optimized anchors. GEO rewards the opposite instinct: clarity. AI models rely heavily on anchor text to predict what a linked page is about, so the anchor should be a concise summary of the destination's primary topic.
Compare three versions of the same link. "Click here to learn more" carries zero semantic value. "See our pricing" is better but thin. "See our AI visibility monitoring pricing plans" tells the model exactly what sits on the other end. The rule of thumb: read the anchor on its own and ask whether it names the destination's main entity. If it does not, rewrite it. Avoid repeating the identical anchor across dozens of links, though, because varied, natural phrasing that still names the entity reads as more trustworthy than a single stuffed phrase.
Step 2: Build a hub-and-spoke structure
Models understand hierarchy well, so give them one. Group content into hubs (broad topics) and spokes (specific details). A hub might be "the complete guide to generative engine optimization." Its spokes are focused pieces like "what is AIGVR," "how to optimize for ChatGPT," and "schema markup for GEO."
Wire them together deliberately. The hub links out to every spoke. Every spoke links back to the hub. And spokes link to related spokes, so "AIGVR" connects to "Share of Model" because a reader, and a model, moving through one naturally needs the other. This structure tells the AI which page is the authoritative center of a topic and how the supporting pieces fit around it. For turning individual articles into strong spokes, see [how to do GEO for blog content](/blog/geo-for-blog-content).
