---
term: "Generative Engine Optimization"
canonical: https://citable.wiki/glossary/generative-engine-optimization
also_known_as: "GEO"
license: CC BY 4.0
---

# Generative Engine Optimization

**Definition:** Generative Engine Optimization (GEO) is the practice of increasing how often, and how favourably, a source is used and cited by generative engines — search systems that answer with text produced by a large language model, such as Google AI Overviews, Perplexity or ChatGPT search. The term was introduced in a 2023 research paper by Aggarwal et al.

GEO is the academic name for the problem AEO practitioners were already working on. The original paper, *GEO: Generative Engine Optimization* ([arXiv:2311.09735](https://arxiv.org/abs/2311.09735)), defined "generative engines" as systems that synthesise an answer from multiple sources with a language model, proposed a benchmark (GEO-bench) and measured which content edits increased a source's share of the generated answer.

Its headline finding is the one most often repeated: **evidence beats keywords**. Content edits that added citations, quotations and statistics improved visibility; edits that added keywords did not.

## Frequently asked questions

### What did the GEO paper find?

The study (Aggarwal et al., 2023, later published at KDD 2024) tested nine content changes on a benchmark of queries. Adding citations to sources, adding quotations from authorities and adding statistics increased a source's visibility in generated answers by up to 40% in their metric, while keyword stuffing had little or negative effect.

## Related guides

- [GEO vs SEO vs AEO: what actually changed](https://citable.wiki/guides/geo-vs-seo-vs-aeo)
- [What is Answer Engine Optimization (AEO)?](https://citable.wiki/guides/what-is-answer-engine-optimization)

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Source: https://citable.wiki/glossary/generative-engine-optimization