What is Generative Engine Optimization (GEO)?

**Generative Engine Optimization (GEO)** is the practice of structuring content, infrastructure, and brand signals so that large language model–based search systems — ChatGPT, Claude, Google's AI Overviews, Perplexity, and others — surface, cite, and attribute your work in their generated answers.

If classical SEO is the discipline of *being found* by search engines, GEO is the discipline of *being trusted* by generators.

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AI Overviews now appear in ~30% of US English Google queries (Jan 2026 baseline). Direct traffic from ChatGPT and Perplexity exceeds 200M sessions per week globally. Brands that are not cited by these systems are losing a discovery channel that did not exist 24 months ago.
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## The four signals every LLM weighs

Across 1,200 test sessions (forthcoming in our State of GEO report), we observed four recurring signals that predict whether a source is cited:

1. **Authority** — is the publisher an authoritative source on this topic?
2. **Specificity** — does the source answer the *exact* question, not a generic version?
3. **Recency** — is the source recently updated (or evergreen with a recent review)?
4. **Structure** — is the answer extractable as a coherent chunk?

## GEO vs. SEO

| Dimension | SEO (classic) | GEO |
|---|---|---|
| Goal | Rank #1 in 10 blue links | Be cited in 1 generated answer |
| Surface | SERP | Generative response |
| Currency | Backlinks | Citations |
| Unit of content | Page | Chunk |
| Measurement | Position, CTR | Citation rate, Share of Voice |
| Time to feedback | Days–weeks | Hours–days |

## What's next

In the next pieces in this series we'll cover:

- [How ChatGPT decides which source to cite](/foundations/how-chatgpt-cites)
- [GEO vs. AEO vs. LLMO: an honest taxonomy](/foundations/geo-aeo-llmo)
- [The anatomy of an LLM answer](/foundations/llm-answer-anatomy)

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