What Is GEO (Generative Engine Optimization) and How Is It Different From SEO

A page can rank on page one of Google and still never get quoted when someone asks ChatGPT the same question in their own words. That gap is what GEO is about, and it is smaller and better-evidenced than most of what gets written about it.

What GEO actually is

GEO, Generative Engine Optimization, is the practice of writing and structuring content so a generative AI system, such as ChatGPT, Google’s AI Overviews or AI Mode, or Perplexity, is more likely to quote, summarise, or cite it directly inside an answer it generates, rather than only listing it as a link among ten blue results. The term itself comes from a specific source: a 2024 paper out of Princeton, Georgia Tech, and IIT Delhi (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande, published at KDD 2024), which built a benchmark for measuring exactly this and tested which content changes actually moved the needle.

That matters because a lot of what circulates about GEO is not from that paper, or from anywhere with a disclosed method at all. This is a topic where the marketing around the term has outpaced the evidence for it, so the rest of this page separates the two: what is actually measured, and what is repeated because it sounds plausible.

GEO is layered on top of SEO, not a replacement for it

The single clearest fact here comes directly from Google. Its own documentation on AI features and websites states plainly that there are no additional requirements to appear in AI Overviews or AI Mode beyond the normal Search technical requirements: a page still needs to be indexed and eligible to show with a snippet. No special schema, no new markup format, no separate submission process. It also does not mention llms.txt, the informally proposed file format some tools have marketed around, at all.

The practical read: if a page is not already showing up in ordinary search with a decent snippet, no amount of GEO effort will get it cited in an AI answer, because it is not in the pool an AI system is drawing from in the first place. GEO is what happens after that baseline is met, not instead of it.

What the evidence says actually moves an AI system’s citations

The KDD 2024 paper is still the only peer-reviewed source measuring this directly, and its findings are more specific than “write good content.” Testing against a Position-Adjusted Word Count metric with a 19.5 baseline score, the interventions that produced the largest measured lift were:

• Adding direct quotations from credible sources — the single largest lift measured, at roughly +41%.
• Replacing vague or qualitative claims with specific statistics — roughly +33%.
• General fluency improvements — roughly +29%.
• Citing sources — roughly +28%.

Keyword stuffing was the one tactic tested that measured as actively harmful, at roughly −8.5%. The paper’s own conclusion is worth repeating: which of these matters most is domain-dependent (statistics mattered more for legal and government topics, quotations more for historical and social topics), so the honest takeaway is “add real numbers and real sources,” not a single universal formula.

One caveat worth stating plainly: this paper is from 2024, tested against the AI systems of that period. Nobody has published an equivalent peer-reviewed replication against 2026-era ChatGPT Search, AI Mode, or Perplexity. Treat the direction as the best evidence available, and the exact percentages as dated rather than current.

Two popular GEO claims that the evidence does not support

“Longer content ranks better in AI answers.” An Ahrefs study covering 560,346 AI Overview answers, 1,677,876 cited URLs, and 174,048 pages with extractable content found a near-zero correlation (0.04 Spearman) between word count and citation. Just over half of all cited pages, 53.4%, were under 1,000 words. There was no meaningful difference in length between pages cited in position 1, 2, or 3. A tight page that answers one question clearly is a legitimate citation target; it does not need padding to compete with a 3,000-word guide.

“Adding schema.org markup gets a page cited.” An Ahrefs study tracked 1,885 pages that added JSON-LD schema markup between August 2025 and March 2026 against 4,000 matched control pages that did not. Google AI Overview citations for the schema group actually declined slightly (a statistically significant −4.6%), while the change for Google AI Mode and ChatGPT was indistinguishable from zero. The honest caveat: every page in that sample already had over 100 AI Overview citations before the test began, so the study measures whether schema pushes an already-visible page higher, not whether it helps a page enter consideration in the first place. Either way, it is not the lever some vendors sell it as.

Where the real opportunity is: unsettled, narrow topics

The most useful finding for a small business is not about writing technique at all. A Semrush study, run with SEO researcher Kevin Indig, tracked 1,094 categories monthly across 50,000 brands and 220,000+ domains inside ChatGPT from January to June 2026. It found that only 15.2% of categories had a clear owner (a single brand appearing in at least 4 of 5 representative buyer prompts with a meaningful lead), and 53.7% were “unsettled” — no brand consistently dominant.

The more counterintuitive finding: low-demand, narrow topics were more likely to have a clear owner (19%) than high-demand, broad ones (11.3%). That inverts how competition normally works in traditional search, where the biggest, most searched terms are also the most defended. In AI answers, a specific, narrow question is often the more winnable target, precisely because almost nobody has written the specific answer yet.

What this means in practice

None of the above requires new tooling or a rewrite of an existing SEO process. It means treating an AI-generated answer as a fourth surface (alongside a results list, Discover, and News) that rewards a page which states its answer plainly near the top, backs claims with a real, dated source rather than a vague assertion, and covers a specific question thoroughly rather than a broad topic shallowly. That is also the reasoning behind giving every article on this site a short, self-contained answer and a sourced set of takeaways near the top rather than burying the answer in paragraph six: not because a markup tag requires it, but because a generative engine reading the page has to be able to lift a correct, standalone answer out of it without the rest of the page for context.