Generative Engine Optimization
Generative Engine Optimization is the practice of improving how visible a source is inside the reply a generative engine writes, that reply being composed from several retrieved documents rather than chosen from a ranked list.
What it actually means
A generative engine does not pick a winner. It retrieves several documents, reads them, and writes one reply that blends them. Your source is not ranked first or tenth; it either contributes to the paragraph or it does not, and it is either named or it is not.
GEO is the work of raising your share of that paragraph. The original paper framed it as a measurable objective, being how much of the generated response is attributable to a given source, which is a different target from a position on a results page, and needs different measurement.
Where the term came from
GEO has the cleanest provenance of any term in this field. Pranjal Aggarwal and five co-authors published "GEO: Generative Engine Optimization" on arXiv on 16 November 2023, and the work was later presented at KDD 2024. The paper defines generative engines, proposes metrics for visibility inside a generated answer, and tests which content changes move them.
Because there is a dated paper with an explicit contribution, this is recorded at high attribution confidence — the strongest rating on this site. Where a term has an actual publication behind it, we say so.
GEO or AEO
The two acronyms are used interchangeably by much of the industry, and the distinction that survives scrutiny is one of emphasis rather than substance. AEO grew out of the older snippet and voice-answer work: being the answer. GEO comes from the academic side and describes being cited inside a multi-source generated paragraph.
In commercial practice the same actions serve both. Anyone selling them as separate services is usually describing one discipline twice.
What the paper found moves the needle
The original research tested content modifications against generative-engine visibility and found that the changes which help are largely the ones that make a source easier to quote and harder to dispute: adding citations to authoritative sources, including relevant statistics, and writing with clear, quotable phrasing.
Notably, tactics borrowed from keyword-era SEO performed poorly. Stuffing terminology did not raise visibility inside a generated answer, which is consistent with what the models are actually doing: retrieving and paraphrasing meaning, not counting words.