Generative Engine Optimization (GEO)

Where does the term GEO come from?

From one paper, which is unusual in this field and worth knowing. GEO: Generative Engine Optimization was submitted by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande in November 2023 and published at KDD 2024. It introduced the phrase, described itself as “the first novel paradigm to aid content creators in improving their content visibility in generative engine responses”, and shipped a benchmark called GEO-bench alongside it.

Almost every definition of GEO you will read traces back to that paper, usually without saying so, and usually carrying one number out of it.

What happened to the 40% figure everyone quotes?

The paper reported that its methods “can boost visibility by up to 40%”, and that sentence has been repeated across the industry ever since. Two things about it are worth stating plainly. First, it was measured in a setting where the candidate documents were placed into the model’s context in advance, so retrieval, which is the hard half of the problem, was not part of the test. Second, it did not replicate.

C-SEO Bench, by Puerto, Gubri, Green, Oh and Yun, accepted at NeurIPS Datasets and Benchmarks 2025, tested the same family of methods and concluded that “most current C-SEO methods are not only largely ineffective but also frequently have a negative impact on document ranking”, while traditional SEO strategies were “significantly more effective”. It also found the effect congests: “as we increase the number of C-SEO adopters, the overall gains decrease, depicting a congested and zero-sum nature of the problem.”

That is not a reason to ignore GEO. It is a reason to be suspicious of anybody selling a multiplier, and to notice that the one thing every replication agreed on is that keyword stuffing measures negative.

Which parts of GEO survived testing?

Two, and neither is a rewrite trick. Relevance to the query the engine actually issued, and position within the document, held up across every replication. That is why this glossary keeps arriving at the same two recommendations: answer at the top, and cover the sub-questions rather than the head term.

• **Answer first.** Extraction is positional, and the opening of a document is where it lands.
• **Cover the query fan-out space**, not the keyword. A page is retrieved for a search the reader never typed.
• **Write concrete, extractable facts.** Real dates, real numbers, real names. This is the part a competitor cannot copy by paraphrasing you.

Related terms

• Answer Engine Optimization (AEO)Answer Engine Optimization is the practice of writing pages so an AI assistant can lift a direct answer out of them.
• LLMO (Large Language Model Optimization)LLMO, or Large Language Model Optimization, is a third name for the practice of getting a brand cited by AI assistants.
• Query Fan-OutQuery fan-out is Google’s own term for how AI Overviews and AI Mode answer a question: the engine breaks the question into subtopics and issues many searches at once, then writes one answer from the results.
• AI CitationAn AI citation is a link an AI assistant attaches to its answer, naming a page as a source.