In practice
The defining constraint is measurement. There is no positional index to query, and any single assistant answer is one sample from a distribution nobody has measured. The only credible method is a fixed panel of prompts, run cold on a schedule across several engines, scored by written rules — which is why so many claims in this category are untestable.
The second defining feature is where the answers come from. Models lean heavily on third-party sources — comparison posts, review platforms, community threads, original research — rather than on any vendor's own pages. A brand with excellent classic SEO can therefore be close to invisible in generative answers until that gap is closed deliberately.
Timelines vary more here than in adjacent disciplines. Crawler access problems resolve in days, content restructuring shows in weeks, and shifting what a model believes about a category takes quarters. No amount of publishing on your own domain shortcuts the third.
Not to be confused with
- AEO
- AEO targets search engines selecting your passage for an answer box. GEO targets models naming your brand in a generated response.
- Geographic SEO
- Unrelated. In this context GEO means generative engine optimisation, not geo-targeting or local search.
Questions
Can GEO be measured at all?
Through sampling, yes. A fixed panel of one to two hundred commercial prompts, run cold across several engines and scored by written rules, produces an aggregate stable enough to trend over time.
Can anyone guarantee ChatGPT will cite us?
No, and anyone promising it is selling something. What can be committed to is a measured baseline, a defined scope, and reporting against agreed indicators.
Why do third-party sources matter so much?
Because models retrieve and were trained on text across the web, and they weight independent sources above vendor claims. Your own pages establish facts; third-party sources establish credibility.