AI Mention Finder

Generative engine optimization (GEO)

GEO is the work of getting your brand named, recommended and cited when AI assistants write the answer instead of listing ten links. Here is what it covers, what the research found and what to do on Monday.

Where the term comes from

"Generative Engine Optimization" was coined in a 2023 paper by Pranjal Aggarwal and co-authors (Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi). They tested how rewriting web content changed how often a search-backed AI cited it. Adding quotations from relevant sources, adding statistics and citing sources raised visibility in their benchmark; stuffing keywords did not help. Treat the exact percentages in the paper as specific to its setup, but the direction is useful: evidence-rich, quotable writing gets picked up.

GEO, AI SEO, LLM SEO: same thing?

In practice, yes. You'll see GEO, AI SEO, LLM SEO, LLM optimization and AI search optimization used for the same goal. Answer engine optimization is the older cousin, focused on direct answers and snippets. For how it relates to classic search work, see GEO vs SEO.

The two routes into an answer

Every GEO tactic helps one of two routes. Step through both in the explainer: models that answer from memory and models that search first. Memory is slow to change and rewards being widely and consistently discussed. Search is fast to change and rewards being retrievable and quotable today.

A GEO checklist

Be findable

  • Rank for the searches an assistant would run on your buyers' questions. GEO sits on top of SEO, not instead of it.
  • Decide on AI crawlers in robots.txt. OpenAI (GPTBot, OAI-SearchBot), Anthropic (ClaudeBot), Perplexity (PerplexityBot) and Google (Google-Extended) publish their user agents. Blocking them blocks citations.
  • Serve key content as plain HTML. Some AI crawlers don't run JavaScript.
  • Offer an llms.txt file pointing to your most useful pages. It's a proposed convention, not a guarantee, and cheap to add.

Be quotable

  • Open each section with a direct, self-contained answer, then support it. The answer-readiness checker scores this.
  • Put facts in text: prices, limits, integrations, who it's for. Models can't quote a screenshot.
  • Cite sources and add specific numbers you can stand behind.

Be talked about

  • Appear in the comparison articles, review sites and community threads that models read and cite.
  • Describe yourself the same way everywhere: same name, same category words. Inconsistent descriptions blur what a model learns.
  • Publish honest comparison pages. Models asked "X vs Y" look for them.

Measure

  • Use a fixed set of unbranded prompts and run the mention check regularly. Read the reasons models give for picking competitors: they are your gap list.

How a brand ends up in an AI answer

At question time

The question becomes searches

Search-first assistants (Perplexity, ChatGPT with search, Google AI features) rewrite the question into one or more web queries.

Questions

What is generative engine optimization (GEO)?
GEO is the practice of making a brand and its content more likely to be mentioned, recommended and cited in answers written by generative AI systems such as ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews. The term comes from a 2023 research paper by Aggarwal and colleagues at Princeton, Georgia Tech, the Allen Institute and IIT Delhi.
Is GEO the same as AI SEO or LLM SEO?
Mostly yes. "AI SEO", "LLM SEO", "LLM optimization", "AI search optimization" and "GEO" are used almost interchangeably. AEO (answer engine optimization) is an older, overlapping term that grew out of featured snippets and voice search.
Does GEO replace SEO?
No. Search-first assistants retrieve pages through web search, so ranking well is still the entry ticket. GEO adds concerns SEO paid less attention to: being quotable, being mentioned on third-party sites, and being consistent enough that a model learns what you are.
How do I measure GEO results?
Track how often a fixed set of unbranded prompts produces answers naming you, across several models, over time. A single check is noisy; a monthly series with the same prompts shows direction.