What Is Generative Engine Optimization (GEO)?

Where the term GEO comes from

The term was introduced in the paper "GEO: Generative Engine Optimization" by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande, with authors at Princeton University and IIT Delhi. It was first posted to arXiv in November 2023 and the arXiv listing notes it was accepted to KDD 2024. The authors framed GEO as a black-box problem: content creators cannot see inside the engine, so they need a way to test which changes to their own pages raise visibility in generated answers. To do that they built GEO-bench, a benchmark of 10,000 queries across 25 domains, paired with web sources the engine could draw on.

How a generative engine picks what to cite

AI answers that cite sources typically follow the same broad pattern. The system turns the question into one or more searches, retrieves candidate pages, reads passages from them, and writes an answer, attaching citations to some of the passages it used. Three things therefore decide whether a page appears. It must be retrievable: crawlable and indexed by whatever search layer the engine uses. It must be extractable: a passage has to answer the question on its own. And it must look credible and relevant next to the other sources retrieved.

Google is blunt about the technical side of its own AI features: there is no special schema to add, no machine-readable feed to publish, and no AI text file to host, and the eligibility rules are the ordinary indexing and snippet rules, set out in what answer engine optimization is. Answers produced from a model's training data, without live retrieval, follow a slower path: they change only when the model is retrained.

What the GEO paper actually found

The authors tested nine content changes: Authoritative tone, Statistics Addition, Keyword Stuffing, Cite Sources, Quotation Addition, Easy-to-Understand, Fluency Optimization, Unique Words, and Technical Terms. Visibility was scored with a position-adjusted word count (how much of the answer drew on a source, weighted toward the top) and a subjective impression score.

Citing sources, adding quotations, and adding statistics performed best, with relative gains of roughly 30 to 40 percent; the abstract's headline is "up to 40%". Keyword stuffing, the classic SEO habit, showed little or no improvement, and scored about 10 percent below the unoptimised baseline on Perplexity.ai. Results varied by domain, and lower-ranked sources gained most: in the paper, Cite Sources raised the visibility of fifth-ranked sources by 115.1 percent while top-ranked sources lost 30.3 percent on average.

Treat these as directional. They come from a benchmark run in 2023, and production engines change their retrieval and ranking often.

GEO compared with SEO and AEO

SEO aims to rank a page so a searcher clicks it. GEO aims to have the page used and named inside the answer itself. Answer engine optimisation, AEO, is an older and broader label that started with featured snippets and voice assistants. The tactics overlap heavily, and a page that cannot be crawled or ranked rarely gets cited. A side-by-side breakdown is in SEO vs generative engine optimization.

What GEO looks like in practice

In practice GEO is mostly discipline. Put a direct, self-contained answer near the top of each page. Back claims with named, dated sources, and quote people by name where a quote adds evidence. State specific facts instead of adjectives. Keep product facts, such as plans, integrations, and supported billing systems, identical across your site, docs, and listings so an engine retrieving any of them gets the same answer. For the page-level steps, see how to do answer engine optimization.

Why partner and affiliate content matters for GEO

Ask an AI assistant "best affiliate tracking tool for a Stripe-billed SaaS" and the sources it cites are frequently third-party reviews, comparisons, and tutorials, which is exactly the content affiliates and partners publish. No published study measures how often, so treat that as an observation rather than a measured share. The paper's finding that lower-ranked sources can gain the most means a well-structured partner review can be quoted ahead of the vendor's own page.

That has two consequences for a partner program. First, accuracy: a partner page with a stale feature list or old pricing will be repeated by the engine, so give partners a current facts page and tell them when it changes. Second, attribution: a reader who meets your product in an AI answer may never click the partner's link and instead search your brand later. Cookie-based tracking misses that buyer. Referral codes bound at signup catch some of it, which is how HyypeFi attributes customers, and scoring partners only on revenue actions keeps citation volume from being mistaken for revenue.

Measuring GEO

There is no rank to check. Teams keep a list of target questions, run them in the assistants their buyers use, and record whether the brand appears, which pages are cited, and how the product is described. Because answers vary between runs, each question is sampled several times and tracked over weeks. Referral traffic from AI tools in analytics gives a second, partial signal.

Where GEO crosses a line

The same levers that help honest pages can be abused. Invented statistics, fake expert endorsements, and networks of pages that cite each other can push false claims into AI answers. Google's spam policies treat mass-produced pages made mainly to manipulate rankings as scaled content abuse, and fabricated reviews are covered by the FTC's rule on fake reviews and testimonials. GEO done properly makes true information easier to quote.