What is generative engine optimization (GEO)?

Generative engine optimization (GEO) is work that aims to get a website's content retrieved, cited, and used in answers written by generative AI systems, such as Google's AI Overviews, ChatGPT, and Perplexity. The term comes from a research paper published at the KDD 2024 conference (opens in a new tab). Later work found that the paper's rewriting methods rarely helped when tested more strictly (opens in a new tab), and that rewriting a page's text can make it harder to retrieve (opens in a new tab).

This page defines the term, explains where it came from, separates what the original study showed from what later research found, and sets GEO beside answer engine optimization and search engine optimization (SEO). Methods and measurement are covered in the complete guide.

Definitions

• Generative engine. The GEO paper's name for a search system that typically answers questions by "synthesizing information from multiple sources and summarizing them using LLMs" (opens in a new tab). An LLM is a large language model. Examples today include Google AI Overviews and AI Mode, ChatGPT search, Perplexity, Gemini, Microsoft Copilot, and Claude when it searches the web.
• Generative engine optimization. A 2026 review of the field defines it as work that "seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines" (opens in a new tab) (preprint).
• Retrieval. The step in which a system picks pages from a search index to use as sources for an answer.
• Retrieval-augmented generation (RAG). Writing an answer from pages retrieved at the time of the question. Google says its AI features use this technique, which it also calls grounding, to retrieve "relevant, up-to-date web pages from our Search index" (opens in a new tab).
• Query fan-out. Google's term for "a set of concurrent, related queries generated by the model" (opens in a new tab) to find more sources for one question.
• Position-adjusted word count. The GEO paper's main outcome measure: the share of an answer's words that came from one source, discounted by where that source's citation appears (opens in a new tab). It measures a share of text, not traffic or sales.

Where the term came from

The term was introduced by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande. Their paper was first posted to arXiv on 16 November 2023 (opens in a new tab) and published in the proceedings of KDD 2024 (opens in a new tab), a peer-reviewed data-mining conference. The abstract states: "we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses." (opens in a new tab)

The paper also built GEO-bench, a benchmark of 10,000 queries drawn from nine sources and covering 25 domains (opens in a new tab). Its headline result was that GEO methods "can boost visibility by up to 40% in generative engine responses" (opens in a new tab). The next two sections explain what that number measured.

Platforms have since adopted the term. Bing's webmaster guidelines say GEO "focuses on content eligibility for grounding and reference in AI responses" (opens in a new tab). Other labels are in use too. A 2025 benchmark uses the name "Conversational Search Engine Optimization (C-SEO)" (opens in a new tab), and Google's guidance names both AEO and GEO (opens in a new tab). A separate page explains how these labels compare, including AIO and LLMO.

What the original study did

The study tested a simulated system, not a live product (opens in a new tab). It worked in four steps:

1. For each query, it took the full text of the top five Google results.
2. It rewrote one of the five pages with a language model, using one of nine methods, such as adding quotations, adding statistics, citing sources, or improving fluency.
3. It asked the gpt-3.5-turbo model to write an answer, with citations, from the five pages.
4. It measured how much of the answer came from the rewritten page.

In these experiments, adding quotations raised a source's share of the generated answer by about 41% and adding statistics by about 31%, in a simulated engine where the page was already retrieved (opens in a new tab) (peer-reviewed). The main results, as changes in position-adjusted word count compared with the unedited page:

The changes are relative to the unedited baseline score of 19.3 in the paper's main results table (opens in a new tab).

The authors also ran a smaller test on Perplexity. For 200 queries, they supplied the sources as uploaded files instead of letting Perplexity search the web, and adding quotations or statistics again raised the rewritten source's share of the answer (opens in a new tab).

What the study did not show

The results are real, but they hold only under the study's conditions. A 2026 review of 45 studies found the paper's gains "valid within its experimental setting but conditional on a source already being present in a fixed context" (opens in a new tab) (preprint). The study did not show that GEO methods:

• Get a page found. Every rewritten page was already one of the five sources, and the authors list effects on search rankings and retrieval as untested (opens in a new tab).
• Work in live products. The main system was simulated, and the Perplexity test used uploaded files instead of live search.
• Last as systems change. One model, gpt-3.5-turbo, wrote every answer, and the authors note that generative engines change over time (opens in a new tab).
• Produce clicks or sales. The outcome was a share of answer text. The same review found that the paper's gains establish "neither organic discoverability nor durable traffic effects" (opens in a new tab).
• Reward accurate content. A language model wrote the added quotations and statistics. The study measured their effect on the answer, not their accuracy.

What later research found

Taken together, the original paper showed that once a page was among a simulated system's sources, some edits changed how much of the answer it supplied. Later work shows that getting into the source set comes first, and that relevance and position matter more than rewriting.

How GEO relates to AEO and SEO

GEO and AEO. Google's guidance says AEO and GEO "are both terms you may see used to describe work specifically focused on improving visibility in AI search experiences" (opens in a new tab). The two labels differ in origin and emphasis, not in method.

GEO and SEO. Google says its AI features are "rooted in our core Search ranking and quality systems" (opens in a new tab), and that "optimizing for generative AI search is optimizing for the search experience, and thus still SEO" (opens in a new tab). Bing says "Bing and Copilot search experiences rely on the same core crawling, indexing, and ranking foundation as traditional search" (opens in a new tab). The two differ in goals and measurement, which the comparison with SEO sets out.

The SEO+ view

This section states AEO HQ's position. It is a recommendation, not a research finding. AEO HQ treats GEO as SEO+: search engine optimization plus three additions that the evidence supports.

Read this way, GEO is not a separate channel. It is SEO judged by one more outcome: whether AI answers retrieve, cite, and correctly use the content.

Common misconceptions

• "GEO raises visibility by 40%." The figure is a maximum from one simulated setup. A 2026 review of 45 studies calls the gains conditional on a source already being present in a fixed context (opens in a new tab) (preprint).
• "GEO replaces SEO." Google's AI Overviews and AI Mode can only show pages that are indexed and eligible to appear with a snippet (opens in a new tab). Google and Bing both describe the work as built on search.
• "GEO means rewriting pages with an AI tool." Rewriting rarely produced significant gains in a 2025 benchmark (opens in a new tab) and hurt retrieval in a 2026 study of a full search pipeline (opens in a new tab).
• "GEO needs special files or markup." Google says "you don't need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search (including its generative AI capabilities), as Google Search itself doesn't use them" (opens in a new tab). The same guide names llms.txt files as one example.
• "Any tactic that gets cited is fair." Google's spam policies cover attempts to manipulate generative AI responses (opens in a new tab). Bing says "attempts to add content designed to manipulate or interfere with language models used by Bing or Copilot may result in reduced visibility or removal from search experiences" (opens in a new tab).

Frequently asked questions

What does GEO stand for?

Generative engine optimization. The acronym comes from the title of the paper that introduced the term, "GEO: Generative Engine Optimization" (opens in a new tab), first posted in November 2023.

What is GEO in SEO?

Google treats it as part of SEO. It says optimizing for generative AI search "is optimizing for the search experience, and thus still SEO" (opens in a new tab). What changes is the outcome being measured: use and citation in AI answers, not rankings and clicks.

Is GEO the same as AEO?

In practice, yes. Google describes both as terms for work "specifically focused on improving visibility in AI search experiences" (opens in a new tab). The labels differ in where they came from, not in the methods they describe.

Can anyone guarantee GEO results?

No. Google says third-party tools "can't guarantee performance" (opens in a new tab), and Bing says "GEO does not guarantee grounding or citations in AI experiences" (opens in a new tab).

How long does GEO take to work?

No controlled study has measured how long it takes for a page to start appearing in AI answers. A page must be indexed first, and Google says crawling can take "anywhere from a few days to a few weeks" (opens in a new tab).

Change log

• September 27, 2026: First published.

Next steps

AEO HQ sells this work at fixed, published prices, from a $499 automated audit to $8,995 for an audit, a plan, and technical implementation. The services page lists what each package includes.