GEO (Generative Engine Optimization) is the practice of improving how often and how favorably your content appears in the responses of a generative engine — an AI system that synthesizes information from multiple sources and summarizes them using LLMs. The term comes from a peer-reviewed research paper, not a marketing deck, and the distinction between what the paper measured and what the industry now calls GEO is important enough to keep straight.

The origin: a paper, not a pitch deck

The term was introduced in “GEO: Generative Engine Optimization” by Pranav Aggarwal et al., published on arXiv in November 2023 (arXiv:2311.09735) and accepted to KDD 2024 (https://arxiv.org/abs/2311.09735). The paper’s framing, in its own terms:

  • Generative engines (ChatGPT, Bing Chat, Perplexity at the time) “synthesize information from multiple sources and summarize them using LLMs” — a different mechanism than ranking and listing, and one with no established optimization discipline when the paper was written.
  • GEO is presented as “the first novel paradigm to aid content creators in improving their content visibility in generative engine responses,” formulated as a black-box optimization framework: the creator does not need to know the engine’s internals, only which changes to their content shift visibility in the output.
  • The paper introduces GEO-bench, a benchmark of content and evaluation set for measuring visibility shifts.

Two properties of that origin matter when you see “GEO” used in the wild:

  1. It is a research term with a narrow measured object. The paper’s metric is visibility in generated responses — whether and how favorably a content source’s claims show up in the engine’s output for a set of prompts. It is not a traffic metric, not a ranking metric, and not a click metric.
  2. It is a single study. Every finding attributed to “the GEO paper” comes from one experimental setup, one period, and a small number of engine configurations. Treat it as the founding evidence, not as established law.

What the study found, honestly labeled

The paper’s headline result, from its abstract: the GEO strategies it tested “can boost visibility by up to 40% in generative engine responses” (Aggarwal et al., arXiv:2311.09735, 2023; KDD 2024). The strategies included adding concise citations, strengthening authoritative wording, and presenting statistical claims — the kind of passage-level changes that make content easier to lift into a summary.

The honest caveats, stated up front because the marketing versions rarely do:

  • Single study. One team, one benchmark, a handful of engine configurations, one period of a fast-moving system. Engines have changed materially since 2023.
  • The 40% is an upper bound within that study — the “up to” figure across its tested strategies, not an expected value you can book against.
  • Visibility is not citation and citation is not traffic. The paper measures presence in responses. Whether a given response drives a referral, or whether a mention is favorable, are separate questions the study doesn’t close.

That said, the direction of the result — that specific, passage-level properties of source content measurably shift what a generative engine says — has not been contradicted by anything published since. It is the empirical backbone for the whole field.

The structural insight the field is still working out

The most useful thing that has come out of post-paper research on this topic is a distinction the original GEO framing didn’t need: retrieval visibility is not the same thing as citation visibility.

An engine first retrieves candidate pages, then selects a subset to ground the answer, and only then cites. A page can be retrieved and never cited, or cited from a hostname-level or entity-level match rather than the exact URL you optimized. The closest published evidence is an observational 2026 study of production ChatGPT web search across model generations (“Understanding ChatGPT Citations: Evidence from Production Web Search Across Model Generations,” arXiv:2610.15382, https://arxiv.org/abs/2610.15382), which found that candidates retrieved more often are also more likely to be cited — and that a supervised model built on two external proxy signals (Google Search results and OpenAI Responses API candidates) predicted citation with meaningful accuracy (AUC above 0.74 for GPT-5.3 and GPT-5.5 at URL, hostname, and entity levels), even though those proxies together recovered only about a quarter of actually-cited URLs. The practical implication the authors draw, and the one we use: GEO work that only optimizes “being retrievable” may not produce citations, and work that only optimizes “being cited” may miss the retrieval step entirely. Both layers have to hold.

This is also why the research term and the commercial practice have drifted apart, which leads to the usage question.

Research usage vs marketing usage

In the paper, GEO = a defined optimization objective against a defined benchmark, with GEO-bench as the measurement instrument.

In industry usage — including much of the “GEO agency” marketing — GEO has become the umbrella name for everything AI-visibility: getting cited in ChatGPT, appearing in Perplexity answers, showing up in Google’s AI Overviews, being mentioned by Claude. Under that usage, “GEO” overlaps almost entirely with AEO (Answer Engine Optimization) — see what is AEO — and the three-term comparison is where we draw the practical lines.

Why does the ambiguity persist? Three reasons:

  • The paper’s term is the more academically defensible one. “Generative engine” is a real, named mechanism; “answer engine” was a voice-search-era marketing phrase. Research papers and serious industry analysis default to GEO.
  • Vendors prefer the newer acronym. A fresh term gets fresh SERPs. If you search “GEO” as a service, you will mostly find vendors, not the paper.
  • The underlying work is genuinely the same. Whether a team calls its practice AEO or GEO, the levers are extractability, entity clarity, corroboration, and retrievability. The label tells you the vendor’s marketing, not the method.

Our usage on this site: AEO for the commercial practice, GEO for the research tradition and the umbrella term, and we say which we mean when it matters.

The plain-language caveat: the acronym GEO collides with geography. In search engines, autocomplete, and most knowledge bases, bare “GEO” resolves to geospatial topics — geographic information systems, geology, the GEO satellite series, and the halo-orbit station points used in deep-space missions. If a customer, a prospect, or a model asks “what is GEO?” without context, the most likely answer is about geoscience.

This has a concrete consequence for the people doing GEO work: your own category’s AI answers about “GEO” are noise-contaminated, which is one reason this series writes out “Generative Engine Optimization” in full on first mention, pairs it with AEO, and targets the unambiguous long-form queries (“what is generative engine optimization”) rather than the bare acronym. The same logic applies to AEO, which collides with Aeronautics (AEO = aeronautics engineering) in some knowledge bases, though less severely.

What this means for a B2B SaaS brand

If you are evaluating GEO as a discipline to invest in, the paper-based framing gives you a cleaner checklist than the vendor framing:

  1. Define your visibility metric first. Which questions, which surfaces, cited or mentioned, favorable or neutral — and what “visible” means in the paper’s sense (presence in the response), not in the traffic sense.
  2. Work both layers. Retrieval (is the page fetched and a candidate?) and citation (is the passage extractable and corroborated?). The arXiv:2610.15382 evidence says they are separate failure modes.
  3. Label your evidence. Single-study findings, observational findings, and your own measurements are three different evidentiary tiers. Any GEO vendor that mixes them without labels is doing it on purpose.

The mechanics of how answers are actually composed — retrieval, selection, generation, citation — are the subject of how AI answers are actually generated. The full series index, including this post’s place in it, is in AEO and GEO in one page.