# Learn: definitions for AI search visibility

> Short definitional pages for the terms used in AI search visibility work. Each page opens with one plain definition, says why the term matters, explains how Asked Thrice measures it where that is true, and gives one number from our own measurements where we have one. They are written to be quoted, by people and by assistants.

Canonical page: https://askedthrice.com/learn

The measurement method behind every number is on the methodology page, and the studies the numbers come from are on the research page.

## Definitions

### [AI visibility](https://askedthrice.com/learn/ai-visibility.md)

AI visibility is how often, and how prominently, AI assistants such as ChatGPT or Claude name a brand when people ask about its category.

### [AI visibility audit](https://askedthrice.com/learn/ai-visibility-audit.md)

An AI visibility audit is a one-time diagnosis of how AI assistants answer the questions a brand's buyers ask, with the raw answers kept as evidence.

### [GEO vs AEO](https://askedthrice.com/learn/geo-vs-aeo.md)

Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) both name the practice of making a brand more likely to be used and named when an engine writes an answer instead of listing links.

### [GEO vs SEO](https://askedthrice.com/learn/geo-vs-seo.md)

SEO is the practice of getting a page to rank in a list of search results, and GEO, or Generative Engine Optimization, is the practice of getting a brand used and named inside the answer a model writes.

### [LLM optimization (LLMO)](https://askedthrice.com/learn/llm-optimization.md)

LLM optimization, usually shortened to LLMO, is the work of making a brand more likely to be named, and described correctly, when a large language model writes an answer about its category.

### [Answer engine optimization (AEO)](https://askedthrice.com/learn/answer-engine-optimization.md)

Answer engine optimization, or AEO, is the practice of getting a brand named in the answer an engine writes, instead of in the list of links below it.

### [Buyer-intent prompts](https://askedthrice.com/learn/buyer-intent-prompts.md)

Buyer-intent prompts are the questions a person asks an AI assistant while deciding what to buy: which options fit a category, how two of them compare, whether a known objection holds, and what works for a specific use case or context.

### [Query fan-out](https://askedthrice.com/learn/query-fan-out.md)

Query fan-out is the way an AI engine expands one question into several related sub-questions, answers each of them, and combines the results into a single response.

### [Share of voice in AI answers](https://askedthrice.com/learn/share-of-voice-in-ai-answers.md)

Share of voice in AI answers is the number of times a brand is named across a set of AI answers, divided by the number of times any brand is named across the same answers.

### [Mention vs citation](https://askedthrice.com/learn/mention-vs-citation.md)

A mention is a brand named in the text of an AI answer; a citation is a source the answer links to, and it exists only when the assistant grounds its answer in web results.

### [How many runs](https://askedthrice.com/learn/how-many-runs.md)

One run of a prompt is a sample, not a measurement: the same question sent to the same model returns a different set of brands often enough that a single answer cannot be read as the model's opinion.

## Related

- [Methodology: how the audit measures](https://askedthrice.com/methodology.md)
- [Research: the studies behind the numbers](https://askedthrice.com/research.md)

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