Generative engine optimization (GEO)

What is generative engine optimization?

Generative engine optimization (GEO) is the practice of improving how often generative AI systems, such as ChatGPT, Gemini, Perplexity, Claude and Google’s AI Overviews, mention your brand and cite your pages in their answers. It applies SEO thinking to answers that are written by a model rather than listed as ten blue links.

The term was popularised by a 2023 academic paper, “GEO: Generative Engine Optimization”, by researchers at Princeton and other institutions, which tested how changes to a page’s text (adding quotations, statistics and citations, for example) affected how visible it was in generated answers. Since then GEO has become a marketing label with a loose meaning. Some people use it for everything that affects AI visibility; others treat it as a synonym for answer engine optimization or LLM SEO. The differences between these terms are mostly emphasis, not substance.

Most of what works in GEO is SEO you would do anyway. AI assistants with web search find sources through a search index, so a page has to be crawlable, indexable and ranking for the queries the assistant runs. The page then has to answer the question plainly, so that a model can lift a clear sentence or fact from it. And because models also learn from what the wider web says about you, your brand mentions on reviews, lists, forums and news sites shape the answer as much as your own pages.

Be careful with GEO advice that promises shortcuts. Google’s own guidance on optimising for its generative AI features says you don’t need special AI text files, special markup or to break content into tiny chunks, and that creating many pages to target every variation of an AI query can violate its scaled content abuse policy. Tactics such as planting fake mentions or hidden instructions for AI models are manipulation and carry the same risks as any other spam.

Why generative engine optimization matters for SEO

A growing share of questions is answered inside an AI interface. When the answer recommends specific brands and links to a handful of sources, being one of them is the new version of ranking on page one. When you are absent, you often don’t find out from your analytics, because there was never a click to measure.

GEO also gives you a reason to fix things that help classic SEO: thin pages, vague answers, missing facts, an unclear brand entity, and a lack of independent sites talking about you. Work that improves citations in AI answers rarely hurts rankings, and the reverse is usually true as well.

How to do generative engine optimization

1. Start with measurement. Pick the questions your customers ask and track whether AI answers mention and cite you, so you have a baseline and know which prompts to work on.
2. Get the SEO basics right. Make sure the pages you want cited are indexed, load fast, are not blocked to the search and AI crawlers you care about, and rank for the underlying queries. See AI crawlers for which bots to allow.
3. Answer the question directly near the top of the page, in a sentence a model could quote. Follow with detail, steps, examples and sources.
4. Add what only you can add: original data, first-hand testing, prices, specifications, expert quotes. Information gain is what makes a page worth citing over the ten others saying the same thing.
5. Keep facts about your brand consistent everywhere: your site, your Google Business Profile, directories, social profiles and press. Models build their picture of your brand from all of them.
6. Earn mentions on the third-party pages that AI answers already cite for your topic, through digital PR, reviews and genuine community participation.
7. Re-measure on the same prompts and models after each change, and judge by trends across many runs.