The Complete Guide to AEO & GEO

1. What is AEO (and GEO / LLMO)?

Answer Engine Optimization (AEO) is the practice of getting your brand surfaced, cited, and recommended inside AI-generated answers — the ones ChatGPT, Perplexity, Claude, Gemini, and Google’s AI Overviews return instead of ten blue links. GEO (Generative Engine Optimization) is the same discipline framed around what a model composes; LLMO is the broader umbrella across training data, retrieval, and synthesis. In practice the three overlap almost entirely.

The mental shift: classic SEO optimizes to rank a page so a human clicks it. AEO optimizes to be the source the model quotes when it writes the answer. The user may never see a link at all.

2. Why AI search changes the game

A ranked list gives ten brands a shot at the click. A synthesized answer names two or three. That collapses the winner set and makes “am I in the answer?” a binary question. It also drives zero-click behavior — the answer is delivered on the surface, so traditional traffic falls even when your visibility rises. Value moves from clicks to citations.

That’s why you can’t manage AI visibility with a rank tracker. You need to know, per prompt and per engine, whether you’re mentioned, whether you’re cited, and how you’re described relative to competitors.

3. How answer engines pick sources

Most modern answer engines follow a version of the same pipeline:

• Fan-out. One question is expanded into many sub-queries.
• Retrieval. For each sub-query, the engine pulls candidate documents — usually by semantic (vector) similarity, not exact keywords.
• Grounding. The model composes an answer tied to those retrieved sources.
• Citation. It attributes claims to a subset of them — the mentions you want to win.

Two consequences follow. First, retrieval means fresh, crawlable, well-structured content can be cited even if it wasn’t in the model’s training data (this is RAG). Second, being understood matters as much as being found: clear entities and structured data make a source safe to quote.

4. The AEO workflow

A repeatable loop beats one-off tactics. Four stages:

• Monitor. Track a real set of buyer prompts across every engine — mention rate, citations, sentiment, and rank.
• Diagnose. For prompts where a competitor is recommended instead of you, find out why — which sources the model leaned on and what they said.
• Improve. Produce answer-shaped, citable content and strengthen the entities and structured data behind it.
• Prove. Watch AI Share of Voice move and tie it back to the work.

5. What to actually optimize

• Entities. Keep your brand, products, and people consistently described across your site and the wider web so models associate you with the right topics.
• Answer-shaped content. Lead with the direct answer, then support it. Definitional and comparison pages are cited disproportionately often.
• Structured data. Schema.org / JSON-LD makes entities and relationships explicit and a page safer to quote.
• Crawlability. Allow the AI crawlers you want to be cited by (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). If they can’t fetch it, they can’t cite it.
• llms.txt. Offer a curated, machine-friendly map of your most important content.
• Third-party presence. Reviews, listings, and reputable mentions are often what the model retrieves — not just your own domain.

6. How to measure AEO

Four metrics carry most of the signal:

• AI Share of Voice — your share of answers vs. competitors on a topic. The headline number.
• Mention rate — how often you appear across tracked prompts and engines.
• Citations — being linked/named as a source, not merely referenced in passing.
• Sentiment — whether you’re framed positively, neutrally, or negatively.

Track them per engine and per language; the same brand can win in Perplexity and be invisible in AI Overviews.

7. Getting started

Start by writing down the 10–20 prompts a real buyer would ask, then measure where you actually stand on each across the major engines. That baseline tells you which prompts to fight for first. Citadex runs this whole loop — monitoring, diagnosis, answer-content generation, and Share-of-Voice reporting — across 11 engines and every language.