AI Internal Linking: Build a Useful Graph, Not a Link Dump

AI can compare many passages and page summaries quickly, which makes it useful for internal-link discovery. The model should propose candidates—not edit the site blindly. A good internal link helps a reader continue a task, points to the preferred live URL, uses understandable anchor text, and strengthens a real relationship between pages.

Start with a trustworthy inventory

Export the canonical URL, title, H1, summary, status code, robots directive, content type, topics, and last meaningful update for every index-eligible page. Exclude drafts, private routes, redirects, errors, search results, and canonicalized-away URLs from the destination pool. If the inventory is wrong, the suggestions will be wrong at scale.

Generate candidates from exact passages

Give the system a source paragraph and a controlled list of destination summaries. Require it to return the source URL, exact passage, destination URL, proposed anchor, relationship, reader benefit, and confidence reason. Suggestions without a source passage are difficult to place naturally and easy to overproduce.

{
  "source_url": "/blog/ai-search-optimization",
  "source_passage": "Track classic Web performance and any available generative-AI reporting...",
  "destination_url": "/blog/ai-search-measurement",
  "anchor": "AI search measurement guide",
  "reader_reason": "Provides the reporting template promised in this section",
  "checks": ["200", "index-eligible", "self-canonical", "not already linked"]
}

Score for relevance and safety

Use crawlable, descriptive links

Google documents ordinary <a href> links as the dependable format for discovery and recommends descriptive anchor text in context. JavaScript click handlers, non-anchor elements, and URLs hidden behind forms are weaker choices for essential navigation. Every page you care about should receive at least one relevant internal link.

Design the cluster before bulk editing

Deploy in small, reviewable batches

1. Validate all destination responses, canonicals, and robots directives immediately before the edit.
2. Add links in the main content where they help comprehension; keep navigation changes separate.
3. Render and inspect the final HTML on desktop and mobile.
4. Crawl the changed pages to confirm the exact anchors and final destinations.
5. Annotate the release and retain the candidate list, accepted links, and rejection reasons.

Measure outcomes without inventing causality

Monitor discovery of previously orphaned pages, query-page impressions, clicks, engagement, and useful actions. A change after deployment is not automatically caused by the links: content updates, demand, crawling, competitors, and result layouts may change at the same time. Compare a suitable window and inspect page-level evidence.

Primary sources

• Google: Link best practices
• Google: SEO Starter Guide
• Google: Optimizing for generative AI features
• Google: Canonicalization guidance