RankX AI blog
GEO Fundamentals
Generative engine optimisation is the work of making a brand retrievable, quotable and named when AI assistants answer buying questions. This cluster covers the fundamentals: how retrieval selects sources, how answers cite them, which page structures survive extraction, and which widely sold tactics fail the moment somebody measures them properly.
Where to start
Generative Engine Optimization: A Complete Guide
Generative Engine Optimization (GEO) is the practice of making a brand and its pages retrievable, quotable and recommendable by generative engines such as ChatGPT, Google AI Overviews, Perplexity, Claude, Gemini and Grok. It extends SEO: the same crawlable, well-structured content, written and organised so generative AI systems can extract it and name you.
More GEO Fundamentals articles
What Is Answer Engine Optimization (AEO)?
Answer Engine Optimization (AEO) is the practice of structuring content so answer engines like ChatGPT, Google AI Overviews and featured snippets can extract it and present it as the answer. In practice that means answer-first pages: a direct answer at the top, question-shaped headings, and sections that survive being quoted alone.
GEO vs SEO vs AEO: What's the Difference?
SEO earns retrieval: crawlable pages, resolvable entities and rankings in a search index. AEO earns extraction: answer-first structure an engine can quote. GEO is the umbrella covering both plus off-page reputation, aimed at AI answers. Prioritise SEO first because it gates the other two, then apply AEO structure to every page.
Query Fan-Out: One Prompt, Twenty Searches
Query fan-out is how AI search engines expand one prompt into many synthetic sub-queries and search for each simultaneously. Google confirmed the technique for AI Overviews and AI Mode in 2025. Pages win citations by matching sub-questions rather than the visible prompt, which is why covering adjacent questions beats chasing the head term.
How AI Crawlers Read Your Site
AI crawlers such as GPTBot, ClaudeBot and PerplexityBot fetch your raw HTML and execute no JavaScript, so content that only appears after scripts run is invisible to them. Each vendor runs separate bots for training, search and user requests, and blocking the wrong one removes you from answers without protecting anything.
What Is llms.txt, and Do You Need One?
llms.txt is a proposed plain-text file at your site root listing the pages AI systems should read, in Markdown. The honest evidence: no major engine documents consuming it, and 97 percent of the files in a 137,000-domain log study received zero requests. Ship one only because it is cheap, never as a visibility lever.
GEO Fundamentals is one topic on the RankX AI blog. Related topics: AI Visibility Measurement and AI Agents and MCP.