LLMention Knowledge Hub

Generative Engine Optimization Guides

Step-by-step technical blueprints to help engineering and SEO teams optimize brand presence in ChatGPT, Perplexity, and Claude.

The guides

Four technical guides. Together they cover the checks the scanner most often fails on a site that is otherwise well built.

GuideTopicRead time
How to generate and deploy an llms.txt fileSetup3 min read
Configuring robots.txt and WAF for GPTBot and PerplexityBotTechnical4 min read
Optimizing headings for direct AI citationContent4 min read
Implementing Schema.org JSON-LD for entity disambiguationSchema5 min read

Questions about these guides

Where should I start?

With the llms.txt deployment guide if you have not published a context file, and with the robots.txt guide if AI crawlers might be turned away at your firewall before robots.txt is even read.

Are the guides specific to one AI engine?

No. They cover the crawler user-agents and markup conventions the major engines have in common, and they say plainly where support is inconsistent.

Do the guides replace the scan?

No, they are complements. A guide explains what a rule wants; the scan tells you whether your page satisfies it, and the scanner applies the same rules these guides describe.

Evidence and sources

Adding source citations produced the largest measured visibility gain for low-ranking sites, at +115%, ahead of the addition of expert quotations at +41% and statistics at +30-40%, across the strategies tested on generative engines. — Generative Engine Optimization, KDD 2024

The weightings on this site follow that measurement rather than taste, and the parts of the picture a single-URL scan cannot see are stated rather than left out.

Primary sources