What it is
Generative Engine Optimization is the work of making your content the material a language model chooses to use when it writes an answer. Classic SEO aims at a position in a list of links. GEO aims at the answer itself: the paragraph ChatGPT, Perplexity, Gemini or Google AI Overviews writes, and the few sources it names beside or beneath it. The name comes from an academic paper that built a benchmark of real queries and measured which rewrites of a source made generative engines use more of it.
Two things decide whether you show up. The engine has to retrieve your page, which is still largely a search and crawling problem, and then it has to find something in the page worth lifting. GEO is mostly about the second step: clear claims, evidence the model can repeat, and structure that makes a passage easy to extract without losing its meaning.
Why it matters
Answers increasingly replace the click. When an AI summary appears, people click through to ordinary results less often, and they rarely click the sources inside the summary either. A page can rank well and still go unread, so being the named source inside the answer is becoming part of what distribution means for anyone selling expertise.
The encouraging part is that the changes with evidence behind them are not tricks. In the GEO research, adding citations, quotations and statistics helped most, while stuffing in keywords did little. Content that is well sourced for human readers tends to be the content engines prefer to quote.
- Up to 40%The GEO paper reports that its methods can boost visibility by up to 40% in generative engine responses.Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024), abstract, 15 Sept 2026
- 30-40%Its top-performing methods, Cite Sources, Quotation Addition and Statistics Addition, achieved a relative improvement of 30-40% on the Position-Adjusted Word Count metric and 15-30% on the Subjective Impression metric.Aggarwal et al., GEO: Generative Engine Optimization, full text, 15 Sept 2026
- The same paper found that keyword stuffing, while widely used for search engine optimisation, offered little to no improvement in generative engine responses.Aggarwal et al., GEO: Generative Engine Optimization, full text, 15 Sept 2026
- 8%Pew Research Center found that Google users who encountered an AI summary clicked a traditional search result in 8% of visits, against 15% of visits when no summary appeared.Pew Research Center, Google users are less likely to click on links when an AI summary appears (July 2025), 15 Sept 2026
How to do it
- 1
List the questions buyers ask before they buy, in their words, and pick the ones where being quoted would change a decision.
- 2
Give each question a section that opens with a direct answer in one or two plain sentences, then supports it.
- 3
Back every claim that matters with a named source, a quotation from someone accountable, or a figure you can defend, and link to where it came from.
- 4
Structure the supporting detail as lists, comparison tables and short labelled sections, so a passage still makes sense when lifted on its own.
- 5
Make sure AI search crawlers can reach the page, then check a sample of prompts regularly to see whether you are named, and by which engines.
Do this on your own site
A 7-day trial on one domain.
Common mistakes
- Treating GEO as keyword placement. The research found keyword stuffing barely moves generative answers, so effort spent there is effort not spent on evidence.
- Measuring a single run of a single prompt. Generated answers vary between runs, so one screenshot says little about whether you are reliably cited.
- Rewriting pages for machines until they read badly for people. Engines lift passages that are clear and well supported, which is the same thing a reader wants.
An example
How the GEO paper tested it
The researchers took real queries, rewrote the candidate source pages in different ways, such as adding citations, adding quotations, adding statistics or stuffing keywords, and measured how much of each version the generative engine used and how prominently. The evidence-adding rewrites beat the others, which is why the paper is the usual reference for what GEO work should prioritise.
Not to be confused with
- Answer Engine Optimization (AEO)
- AEO grew out of optimising for single-answer surfaces such as featured snippets and voice results. The core work is the same as GEO; AEO emphasises being extracted as the direct answer, GEO emphasises being used and named inside a generated response.
- LLM Optimization (LLMO)
- LLMO is an umbrella label for making a brand show up well in large language model outputs, including what a model says about you without retrieving a page. GEO is narrower: shaping the content that generative engines retrieve and quote.