Generative Engine Optimization: What the GEO Paper Actually Shows for Your Business

Tools like Perplexity, ChatGPT Search, Google AI Overviews, and Microsoft Copilot don't hand users a list of links. They read your content, synthesize it, and write an answer. Whether your website gets mentioned in that answer, and how prominently, is the new battleground for AEO, AI SEO, and AI visibility.

Researchers from Princeton University and IIT Delhi published "GEO: Generative Engine Optimization" at KDD 2024, one of the premier academic conferences in data science and machine learning. They ran 10,000 queries through AI search systems, tested nine content modification strategies, and measured their precise impact on visibility inside AI-generated answers. What follows is a breakdown of every major finding and what it means for your content strategy.

Key Takeaways

The research translates into six concrete practices:

• Cite every substantial claim. The Cite Sources method produced consistent gains across domains and became more powerful in combination with other strategies. Treat every verifiable assertion as something that needs an attributed source.
• Replace qualitative statements with specific numbers. "Many companies struggle with this" is far less useful to a generative engine than a named study with a precise figure. Build the habit of adding measurable evidence wherever you currently use vague descriptions.
• Include relevant expert quotes. Quotation Addition was the top single strategy. Attributed quotes from researchers, practitioners, and industry reports give language models concrete, citable material to incorporate.
• Write clearly. Fluency improvements produced a 28% gain with no new content added. Well-structured, readable prose is easier for a model to parse, summarize, and attribute accurately.
• Match your strategy to your domain. Law and government content benefits from statistics. People, society and history content benefits from quotes. Debate content benefits from a confident, authoritative tone.
• Combine tactics rather than relying on one. The combination of Fluency Optimization and Statistics Addition outperformed any single method by more than 5.5% on 200 test queries. Sustainable AI visibility comes from multiple good practices working together, not a single optimization pass.

Who invented generative engine optimization (GEO)?

Generative engine optimization was introduced by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande in the paper “GEO: Generative Engine Optimization”. They first posted it on arXiv on 16 November 2023, and it was published at KDD 2024.

The paper describes GEO as “the first novel paradigm to aid content creators” in improving how visible their content is inside AI-generated answers. When people search for “the GEO paper”, this is the paper they mean, and it is the one this article breaks down.

Is AEO the same as GEO?

Yes, in practice, AEO is the same as GEO. Answer engine optimization (AEO) and generative engine optimization (GEO) are two names for the same work: making your pages easy for AI engines to find, read, cite and describe correctly.

The names come from different places. GEO was introduced by the paper this article breaks down, which gave the work a formal definition and a benchmark (GEO-bench); AEO grew out of SEO practice. Google treats them as one job: its guide to AI search calls both “terms you may see used to describe work specifically focused on improving visibility in AI search experiences” and says that, for Google Search, the work is “still SEO” (Google Search Central).

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How AI Search Engines Actually Work

Generative engines answer in four steps, according to the GEO paper: a model rewrites the query into several sub-queries, a search engine retrieves a set of sources (the top 5 Google results in the paper's experiments), a summarization model reads each source, and a response model writes one answer with inline citations. A page competes to be quoted in that answer, not to rank.

To optimize for a system, you need to understand it. The paper provides a precise picture of how generative engines operate. When a user submits a query, the system runs a multi-step process:

• A query reformulation model rewrites the input into several search-friendly sub-queries
• A search engine retrieves sources for those queries (the paper's experiments used the top 5 Google results)
• A summarization model reads each source
• A response-generation model synthesizes everything into a coherent answer with inline citations

The researchers formalize this as: a Generative Engine (GE) takes a user query qu and personalized user information PU and returns a natural language response r:

The critical observation: you are not competing for a ranked position on a results page. You are competing to be the source a language model chooses to quote, paraphrase, or attribute when it writes its answer. That is a fundamentally different challenge from traditional SEO.

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Rethinking What Visibility Means in AI Search

In traditional search, visibility is simple: where does your page rank? In AI search, that metric breaks down entirely. A generative engine writes a paragraph, weaves in citations from multiple sources, and might reference your content heavily in one answer and barely at all in the next.

The paper introduces two new metrics designed specifically for this environment:

Position-Adjusted Word Count

This measures how many words in the AI's response were attributed to your source, weighted by where in the response they appeared. Citations early in an answer receive more weight than those near the end, reflecting research showing that user attention follows a power-law distribution by position.

Subjective Impression

This qualitative metric, evaluated using the G-Eval methodology, scores a citation across seven dimensions: relevance to the query, influence on the response's logic, uniqueness, positional prominence, volume contributed, likelihood of a click, and information diversity.

How the Study Was Conducted: GEO-bench

Because no benchmark existed for this problem, the researchers built one. GEO-bench contains 10,000 queries drawn from nine sources: real queries from Bing and Google, essay questions from Oxford's All Souls College, the LIMA reasoning dataset, debate questions, trending Perplexity.ai queries, Reddit, and GPT-4-generated queries. It spans 25 domains with informational, transactional, and navigational queries.

For each query, they fetched the top 5 Google results and used GPT-3.5-turbo to generate an answer grounded in those sources. They then applied each GEO method to one randomly selected source, re-ran the generation, and measured the visibility change. The study measured visibility inside AI-generated answers on two generative engines, not traffic or conversions.

What Actually Works: The 9 GEO Methods

The GEO paper tested nine content strategies on GEO-bench. Quotation Addition worked best, improving Position-Adjusted Word Count by 41% over the unmodified baseline, followed by Statistics Addition at 31% and by Cite Sources and Fluency Optimization at roughly 28% each. Keyword stuffing scored 8% below the baseline.

The paper evaluates nine content strategies. All relative improvements below are calculated against the unmodified baseline on the Position-Adjusted Word Count metric, drawn directly from Table 1 of the paper.

The Top Strategies: Quotations, Statistics, and Citing Sources

Quotation Addition was the single highest-performing method, improving Position-Adjusted Word Count by 41% and Subjective Impression by 28% over baseline. Statistics Addition improved the same metrics by 31% and 23% respectively. Cite Sources came in close behind on both.

The underlying logic is consistent across all three: when a language model synthesizes an answer from multiple sources, it gravitates toward content that provides something concrete and attributable. A direct quote from a named expert, a specific percentage, or an explicit citation gives the model a discrete, verifiable unit to include. Vague, qualitative prose gives it much less to work with.

"Including citations, quotations from relevant sources, and statistics can significantly boost source visibility, with an increase of over 40% across various queries."
-- Aggarwal et al., GEO: Generative Engine Optimization, KDD 2024

Fluency Matters More Than Most Expect

Fluency Optimization and Cite Sources both produced roughly 28% gains on the Position-Adjusted Word Count metric. This is notable because fluency is a stylistic improvement with no new content added. The result suggests that clear, well-constructed prose is easier for a language model to parse, summarize, and attribute accurately. Dense or convoluted writing appears to work against the source, even when the underlying information is strong.

What Does Not Work: Keyword Stuffing

Keyword stuffing, the tactic of loading content with repeated query terms, scored 8% below the unmodified baseline on Position-Adjusted Word Count. In the Perplexity.ai validation, it performed 10% below baseline. This finding is consistent: generative engines use language models that understand semantics, not keyword frequency. Stuffed content reads as lower quality to the model summarizing it.

Domain Matters: Strategy Should Match Topic

GEO methods do not work equally well in every subject. The paper's Table 3 lists, for each method, the query categories where it helped most: statistics on law and government, debate and opinion queries; expert quotations on people and society, explanation and history queries; and a confident, authoritative tone on debate, history and science queries.

One of the more nuanced findings is that GEO method effectiveness varies by content domain. There is no universal tactic. The paper's Table 3 shows the three query categories where each method performed best:

The practical implication, as the paper puts it, is to choose methods by domain: law and government content benefits from statistics, people, society and history content from expert quotations, and debate content from a confident, authoritative tone.

Combining Strategies Outperforms Any Single Tactic

The researchers tested all pair combinations of the four top-performing methods: Cite Sources, Fluency Optimization, Statistics Addition, and Quotation Addition. The best single method (Quotation Addition) produced roughly 40% gains. Combining Fluency Optimization with Statistics Addition outperformed any single strategy by more than 5.5%, measured on a subset of 200 test queries. Cite Sources, moderately effective alone, became substantially stronger when combined with any of the other three.

The takeaway: strong AI visibility is unlikely to come from one tactic applied in isolation. Content that is clear, well-cited, evidence-rich, and includes relevant expert quotes performs better than content optimized on any single dimension.

GEO (AEO) Can Level the Playing Field for Smaller Businesses and Publishers

GEO helps lower-ranked sites most. When the researchers optimized every competing source for a query, a fifth-ranked website gained 115.1% in AI visibility from Cite Sources alone, while the top-ranked site lost 30.3% of its share. Generative engines condition on content quality directly, so well-cited pages can compete regardless of domain authority.

Perhaps the most surprising finding that comes from this paper, where the researchers optimized all competing sources for a query simultaneously. The results show a stark difference in how GEO affects sites based on their original search engine ranking:

A fifth-ranked website, typically starved for traffic in traditional search, saw a 115.1% increase in AI visibility from Cite Sources alone. Meanwhile the top-ranked site lost 30.3% of its AI response share in the same scenario. The paper's explanation is that traditional search heavily weights off-page signals like backlinks, which favor large incumbents. Generative engines condition on content quality directly, which means well-structured, well-cited content can compete regardless of domain authority.

This does not eliminate the need for traditional SEO. Sources must still be retrieved before they can be cited. But once in the retrieval set, GEO methods can significantly change which sources the model draws on most heavily. Retrieval is only one of the eight places where a company can drop out of AI answers, and each one has a different fix.

Real-World Validation on Perplexity.ai

The GEO researchers also tested the methods on Perplexity.ai, using 200 queries from the GEO-bench test set and giving Perplexity the source text as uploaded files to answer from. The results held up: Quotation Addition improved position-adjusted visibility by 22% over baseline, Statistics Addition improved the subjective impression metric by 37%, and keyword stuffing came in 10% below the unoptimized baseline.

The pattern is consistent across both environments.

What kind of content do AI engines cite?

AI engines cite pages that give them something specific to quote: a clear answer, a number with its source, or the words of someone with first-hand experience. In the GEO benchmark above, adding quotations, statistics and citations to credible sources were the methods that raised a source's visibility most, and keyword stuffing did not help.

Google gives the same advice for its own AI features in plainer terms: make unique, non-commodity content that visitors find helpful and satisfying (Google Search Central, May 2025, verified October 21, 2026). In practice that means four kinds of material:

• Original data. Numbers you collected yourself, published with the method and the number of cases.
• Named experience. What you or your team saw first-hand, attributed to a real person.
• Sourced statistics. Someone else's figures, linked to the study they come from.
• Direct answers. A short answer right under each question heading, so it can be quoted on its own.

Can a small business create original data that AI will cite?

Yes, a small business can create original data, and it is often the cheapest way to say something no competitor has said. A short survey of your own customers, a count from your own job records, or a simple before-and-after test on your own site all qualify, as long as you publish how you collected the numbers and how many cases they cover. No engine promises to cite it, but a number nobody else has gives it a reason to.

Technical GEO checklist: how to prepare a website for AI search

Preparing a website for AI search with GEO means two things: making sure AI search engines can retrieve your pages, and then giving them content worth quoting. The GEO paper covers the second part, so the checklist below starts with retrieval and then turns the paper's best methods into page-level steps. To choose which pages to prepare first, start from the questions your buyers ask AI assistants: our guide to prompt research shows how to find them.

• Let AI search crawlers in. Sources must be retrieved before they can be cited. Check that your robots.txt allows the search crawlers and user fetchers of the engines you care about. The AI crawler check in our free AEO tools reads your robots.txt and lists them engine by engine.
• Put the main text in the HTML. Keep each answer in the page source, so a crawler that does not run JavaScript still reads it.
• Cite your sources. Link every substantial claim to its source (Cite Sources).
• Use specific numbers. Replace vague statements with figures and name where each one comes from (Statistics Addition).
• Quote named experts. Add attributed quotes where they fit your topic (Quotation Addition, the strongest single method in the paper).
• Write fluently. Clear, well-constructed sentences are easier for a model to summarize and attribute (Fluency Optimization).
• Skip keyword stuffing. It scored below the unmodified baseline in both test environments.
• Match the method to your topic. Use the domain table above to see which methods did best on topics like yours.
• Keep structured data true to the page. Any markup you add should match the visible text; see our schema markup guide.

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What does the GEO paper mean for your content?

This is one of the most rigorous empirical studies published on what drives visibility inside AI-generated search responses. The core message: AEO, GEO, or AI search rewards citation-worthiness, not keyword density. Content that gets cited in AI answers is specific, well-sourced, clearly written, and evidence-rich. Content that does not get cited is vague, repetitive, and hard for a language model to attribute with confidence.

The most important mindset shift is this: your content is no longer just a destination for human readers. In a world where AI agents research on behalf of users, your content functions as a data source for machines as much as an experience for people. Businesses that build their content with that dual audience in mind are the ones that will show up in the answers their customers are already getting from AI.

Written byDr. Maziar "Maz" GomrokchiCo-Founder and CEO

AI/ML scientist with 20+ years of research & industry experience. Ex-Google DeepMind, MIT, Harvard, Mila. McGill-Mila PhD.

Reviewed bySoroush MehriCo-Founder and Chief AI Officer

Ex-Microsoft. 10+ years of experience in AI/ML, MSc from Université de Montréal (Mila). US patent holder.