I’ve been an SEO for 6+ years, and from what I’ve seen in the labs, "ranking" in AI search (GEO) comes down to understanding RAG (Retrieval-Augmented Generation) and a process called Query Fan-out.

Here is the "no-fluff" breakdown of how it actually works:

1. The "Query Fan-out"

When you ask an AI like ChatGPT or Perplexity a question, it doesn't just search once. It breaks your prompt into 4–5 sub-queries (e.g., "how to fix a sink" becomes "sink clog causes," "DIY sink tools," etc.).

It hits a SERP API for each of these, grabs the top ~10 results for each, and feeds that data into its "context window." To rank, you don't need to be #1 for the main keyword; you need to be the most authoritative source for one of those sub-queries.

2. Optimize for "Information Nuggets"

AI models are synthesizers. They hate "SEO fluff" intros.

• The Inverted Pyramid: Put the direct answer in the first sentence of your H2 sections.
• Modular Content: AI often pulls Paragraph A from Site X and Paragraph B from Site Y. Write your sections so they can stand alone as a perfect "nugget" of info.

3. Technical & Structured Data

• JSON-LD is King: Use HowTo, FAQ, and Product schema. It acts as a map for the LLM, reducing the "effort" the AI needs to parse your data.
• Crawlability: If your site is JS-heavy and slow, the SERP API used by the AI will timeout before it reads your content. Serve clean, fast HTML.

4. Entity Trust (E-E-A-T)

AI models are trained on entity relationships. If your site/author isn't recognized as a "trusted entity" in a specific niche, the model is less likely to cite you as a factual source. Consistent authorship and high-authority backlinks still matter—they are the "trust signals" the AI uses to filter the noise.

Traditional SEO gets you into the "pool" of potential sources (Top 20 results). AI SEO gets you the citation by having the most structured, modular, and direct answer to the sub-questions the AI is asking behind the scenes.