What is Generative Engine Optimization (GEO)?
An honest taxonomy of the discipline that is reshaping how the web is read — by both humans and machines.
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.
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:
- Authority — is the publisher an authoritative source on this topic?
- Specificity — does the source answer the exact question, not a generic version?
- Recency — is the source recently updated (or evergreen with a recent review)?
- 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
This is the opening piece of the Foundations series. For the full map of the discipline, see the complete taxonomy of GEO methods — every method grouped into five families and rated by evidence quality. For a concrete, technical starting point, our llms.txt adoption audit measures how one infrastructure signal plays out across 100 domains. Coming next in this series:
- How ChatGPT decides which source to cite
- GEO vs. AEO vs. LLMO: an honest taxonomy
- Knowledge cutoff, web access, and why it matters
- The anatomy of an LLM answer
If you'd rather have these in your inbox: subscribe to the newsletter — one weekly briefing, no fluff.
Cite this article
Reference this work in one of the formats below. The same strings are embedded in this page's Schema.org JSON-LD so LLM crawlers see them too.
GeoSalience (2026, May 17). What is Generative Engine Optimization (GEO)?. GeoSalience. https://geosalience.com/foundations/what-is-geo
Changelog
- Published — 17 May 2026
- Updated — 31 May 2026
- Last reviewed — 31 May 2026
Editorial
Independent publication on Generative Engine Optimization. Primary research on how AI search engines retrieve, rank, and cite.
Related
GEO vs AEO vs LLMO vs SGE: An Honest Taxonomy
Four acronyms, mostly the same thing — and a few clear distinctions worth keeping. We pulled the original definitions, checked who uses which term, and argue which one should win.
How to Get Cited by LLMs: The Complete Taxonomy of GEO Methods
Every method GEO practitioners use to surface in ChatGPT, Claude, Perplexity, and AI Overviews — grouped into five families and rated by evidence quality. A synthesis of the published literature, vendor docs, and our own audits. The map of the discipline as of June 2026.
Knowledge Cutoff, Web Access, and Why It Matters
An LLM that browses the web in 2026 still answers from a training anchor months — sometimes a year or more — in the past. Knowing which knowledge comes from where, and how the two interact, is the prerequisite for any GEO strategy.