Technical SEO for AI search decides whether search engines and AI systems can reach, trust and cite your pages at all. The common failures are a robots.txt group that blocks more than intended, a fake crawler you mistake for a real one, a canonical Google overrides, and a file such as llms.txt treated as a ranking lever. These guides and tools cover each one, and the crawler directory gives the operator-documented facts for each named bot.
I need the technical layer to help my content get found and cited, not silently block it.
Block AI crawlers by name, one operator token at a time, and keep Googlebot and Bingbot out of those groups. Most AI operators split training, search and user fetches into separate tokens, so you can refuse training and stay in AI search. Three rule sets by goal, the operator split table, and the catches that change what each rule does.
A request that says it is GPTBot, ClaudeBot or Googlebot proves nothing until you check it the way the operator documents: an IP match against the published file, a reverse DNS lookup confirmed by a forward lookup, or a signature check. Here is the procedure, which of 40 crawlers support each method, and what an IP match can and cannot tell you.
Adding llms.txt is not a required SEO task and should not be treated as a ranking factor, indexing control, robots.txt replacement, or guaranteed path into AI answers. Consider it only as a small documentation experiment: a Markdown file that may help agents understand which pages, docs, or resources matter. If your site already has crawlable, indexable, useful pages and you can publish a simple file without delaying proven SEO work, run a low-risk test. If basic technical SEO or content quality is weak, fix that first.
When Search Console's Google-selected canonical does not match your declared canonical, check content duplication and technical signals before rewriting rel=canonical tags.
An AI search technical SEO audit checks whether important pages can be crawled, rendered, indexed, canonicalized, understood through honest structured data, linked internally, and verified through visible sources. It does not guarantee AI citations or rankings; it removes technical and evidence blockers that prevent good content from being discovered and quoted accurately.