What is Generative Engine Optimization (GEO)? A plain-language guide

Generative Engine Optimization (GEO) is the practice of structuring a website's content so it gets read, understood and cited by AI answer engines: ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews. Where classic SEO optimizes to appear in a list of blue links, GEO optimizes to become the source an AI quotes and attributes inside its answer.

The term was popularized by a 2023 academic study (Aggarwal et al., presented at KDD 2024) that measured, across 10,000 queries, which content modifications increase a source's visibility inside generated answers. One clarification: bare “GEO” is ambiguous (geography, the GEO Group stock, genomics databases) — in a marketing context it always means generative engine optimization.

SEO and GEO share a common base (a crawlable, indexed, fast, well-structured site) but aim at two different outcomes: SEO optimizes the click, GEO optimizes the citation.

GEO does not replace SEO — it stacks on top, because AI engines often retrieve their candidates from classic search indexes. A site invisible in SEO stays invisible in GEO.

Because clicks are moving from links to answers, and the numbers are steep. Seer Interactive measured a 61% drop in organic CTR when a Google AI Overview is present; Ahrefs measured a 58% reduction in clicks on the top result; Pew Research Center found users click a traditional link roughly half as often when an AI summary appears.

The flip side is the opportunity: brands cited inside AI answers capture a disproportionate share of the remaining attention. If the AI answers on the user's behalf and cites only a few sources, being one of them is no longer a bonus — it is the condition for existing. And because adoption is recent, the field is still lightly contested: starting early gains a measurable head start.

The reference GEO study (Aggarwal et al., KDD 2024) tested nine strategies across 10,000 queries. The winners are concrete: adding statistics improved visibility by around 41%, adding quotations by around 28%, and citing credible external sources also produced clear gains. Keyword stuffing did nothing or hurt.

In practice, five levers:

1. Explicitly allow AI crawlers in robots.txt.
2. Publish an llms.txt file at your root.
3. Add Q&A schema.org markup (JSON-LD).
4. Structure pages as questions with direct 40-160 word answers backed by real facts.
5. Show an accurate updated date and dateModified (freshness is a signal).

They overlap but are not identical. SEO targets ranking in classic search engines; AEO (Answer Engine Optimization) targets providing the direct answer to a question; GEO specifically targets inclusion and citation inside content generated by large language models. Many people use GEO and AEO interchangeably — the techniques overlap heavily (direct answers, structured content, schema.org, verifiable facts).

The useful distinction is strategic: are you optimizing for a click from a list (SEO), a boxed answer in a SERP (AEO), or a citation inside an AI conversation (GEO)? For most sites, treat all three as a continuum: apply the shared fundamentals, then add the signals specific to generative engines.

Start with an audit: are your pages crawlable by AI bots, do you have an llms.txt, are your answers structured and sourced? Then apply the levers in increasing order of effort — robots.txt, llms.txt, schema.org markup, editorial restructuring, freshness signals.

Two ways: manually (follow the spec, write the JSON-LD yourself, keep it updated), or automatically. Citeable does the second: paste your URL, it analyzes your public pages and generates both your llms.txt and your Q&A schema.org markup, consistent with each other, for a one-time payment and regenerable for life.

Honest scope: nobody controls the answer engines, so no tool or agency can guarantee a citation — it is a best-efforts obligation. What GEO guarantees is that your site is as readable and citable as possible.