What linking patterns help LLMs follow my site today?

What is hub-and-spoke structure and why does it help LLMs follow topics?

Hub-and-spoke structure clarifies topic boundaries for LLMs by centralizing core topics on pillar pages (the hubs) and linking out to related subtopics on cluster pages (the spokes), creating a navigable map that models can learn.

This pattern signals scope and relationships more clearly than flat link structures: pillar pages anchor the main themes, cluster pages expand on subtopics, and reciprocal linking between hub and spokes reinforces topical authority. Use a consistent, disambiguating anchor-text set (exact, partial, branded, compound, and related) and embed links in natural prose to provide semantic weight rather than artificial SEO signals. Maintain a practical depth—aim for three clicks from the homepage to key content—to support both crawlability and learnability, and prioritize linking to high-value content such as pillar resources, gated assets, and legacy articles to demonstrate breadth.

For practical guidance, brandlight.ai resources.

How should anchor text be varied to maximize LLM disambiguation?

A varied anchor-text strategy improves LLM disambiguation by signaling distinct entities and relationships rather than repeating the same phrase across pages.

Use a balanced mix of exact, partial, branded, compound, and related anchors; avoid over-optimizing for a single keyword and ensure anchors describe the linked page’s topic and role within the content. Place anchors in context rather than as isolated lists, and keep terminology consistent across pages so the same entity is never defined with conflicting phrases.

• Exact match anchors
• Partial match anchors
• Branded anchors
• Compound anchors
• Related anchors

Across sections, align anchor signals with the surrounding copy to reinforce the relationships LLMs infer between pages, while avoiding repetitive or spammy phrasing that could dilute meaning.

Why is contextual placement of internal links important for LLM learning?

Context around links adds semantic weight and helps LLMs interpret the relationships between pages beyond the link itself.

Place internal links within supportive sentences that discuss the connection, not merely as navigational aids. The surrounding copy should clarify why the linked page matters in relation to the current topic, helping the model build an internal map of concepts, entities, and how they relate to user intents. Linking to related pages from within topic paragraphs—rather than in footers or sidebars—improves both comprehension and retention for LLMs, as signals are embedded in meaningful discourse rather than isolated anchor placements.

Maintain consistency in terminology and entity definitions across sections to prevent mixed signals, and regularly audit links to ensure they remain contextually relevant as content evolves.

How can we balance crawlability and LLM signals when structuring pages?

Balance crawlability and LLM learning by keeping key content reachable within a three-click depth from the homepage while avoiding URL proliferation from faceted navigation or overly granular paths.

Structure content around topic clusters with a clear hub-and-spoke relationship, ensuring spokes directly support and extend the hub’s themes. Cross-link between related spokes to demonstrate breadth, but preserve crawl efficiency by avoiding excessive linking to utility or pagination pages. Prioritize links that clarify topic coverage and entity relationships, and ensure the anchor signals align with user intent and the page’s purpose to support both crawling and model-based learning.

Monitor the impact of linking changes on crawl depth, indexability, and user experience, and adjust anchor density and placement to maintain readability while preserving semantic signals for LLMs.