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GEO (Generative Engine Optimization)

Updated: 6 min read SEOFuxx editorial team

GEO (Generative Engine Optimization) covers all measures that help content appear in the answers of generative AI systems such as ChatGPT, Perplexity, Gemini, Microsoft Copilot or Google AI Overviews. The goal is for an AI system to link your website as a source, adopt its statements or name your brand.

Where the term comes from

The term goes back to a 2023 research paper: "GEO: Generative Engine Optimization" by Pranjal Aggarwal and co-authors (Princeton University, Georgia Tech, the Allen Institute for AI and IIT Delhi), presented at the KDD conference in 2024. Using their own test data set, the authors examined which text changes affect how visible a source is in generated answers.

The key finding: citing sources, adding quotations and including concrete figures increased visibility by up to around 40 percent, depending on the subject area. Classic keyword stuffing, by contrast, brought hardly any improvement. The figures come from a lab setting and cannot be transferred one-to-one to ChatGPT or Google, but the direction has been confirmed many times since.

How generative search systems build answers

To understand GEO, it helps to look at the typical process. Most AI search systems follow the principle of retrieval-augmented generation (RAG):

  1. Break down the query: the system translates the question into one or more search queries. For AI Mode and AI Overviews, Google describes a method called query fan-out, in which many sub-queries run in parallel.
  2. Retrieve sources: for each sub-query, documents are loaded from a search index, for example the Google or Bing index or the provider's own index.
  3. Select passages: the documents are split into sections (chunking). What is evaluated is which passage best answers the question, not which page is best overall.
  4. Write the answer: the language model writes an answer from the selected passages and refers to the sources it used.

In addition, the knowledge a model brings from its training data plays a role. This knowledge helps determine which brands a model knows at all when answering general questions such as "Which providers are there for …?".

This results in two levers: your content must be found in the retrieval step, and individual sections must be written so that they can be used as an answer.

GEO and SEO compared

GEO does not replace classic search engine optimisation. Many AI systems rely on conventional search indexes; a page that is not indexed or plays no role for a topic is rarely cited. The focus does shift, however:

AspectClassic SEOGEO
ResultPosition in a list of resultsMention or citation in an answer
Unit evaluatedPageIndividual passage
SuccessRankings, impressions, clicksMentions, citations, share of model
Important signalsRelevance, links, technical setup, user experienceAdditionally: verifiability, clarity, brand mentions across the web
Click to the websiteGoal of the optimisationPossible, but not guaranteed

Several terms are in use for the same field. They are often used interchangeably:

  • AEO (Answer Engine Optimization) is the older term and also includes non-generative answer formats such as featured snippets and voice assistants.
  • LLMO (Large Language Model Optimization) emphasises the language models themselves, including their training knowledge.
  • AI SEO is an umbrella term that refers either to optimising for AI search or to using AI tools for SEO tasks.

What influences the choice of sources

No provider publishes exactly how sources are selected. However, several factors can be derived from provider documentation, studies and observation:

  • Findability: the page is indexed and the AI providers' crawlers are allowed to fetch it (see AI crawlers). For AI Overviews, Google only states that a page must be indexed and eligible to be shown with a snippet.
  • Precise answers: a section answers a specific question directly and can be understood without the rest of the page.
  • Verifiability: concrete figures, source references, dates and an identifiable responsible person make statements verifiable.
  • Clarity: people, companies, products and places are clearly named (see Entities and Knowledge Graph).
  • Brand mentions: if a brand is mentioned in articles, comparisons, forums and review platforms in connection with a topic, a model is more likely to name it too (see Brand mentions).
  • Original information: content that offers something missing elsewhere, such as own data or practical experience, has an advantage over interchangeable texts (see Information gain).
  • Technical readability: many AI crawlers do not execute JavaScript. They may not see content that is only loaded in the browser.

Approach in practice

  1. Measure the starting point: compile a list of typical questions from your audience and check how different AI systems answer them: is your brand named, is your website cited, which competitors appear? Repeat these queries regularly (see Prompt tracking).
  2. Check the technical setup: check in robots.txt which AI crawlers are allowed. Make sure important pages are indexed by Google and Bing and that their text is in the delivered HTML.
  3. Structure content: start sections with the key statement, use meaningful subheadings and write sections so that they can be understood on their own. Tables, lists and clear definitions are easy to adopt.
  4. Back up statements: add figures, sources, dates and author information. Update content when facts change.
  5. Mark up entities: describe your company with structured data (for example Organization with sameAs references to official profiles) and keep information consistent across all platforms.
  6. Become visible beyond your own website: expert articles, mentions in comparisons, reviews and forum discussions help models associate your brand with a topic.
  7. Monitor the effect: compare the values from step 1 over weeks and months and also evaluate referral traffic from AI services.

Measuring success

Classic metrics such as rankings only partly capture GEO. Common metrics instead are:

  • Mention rate: share of answers in which your brand is named
  • Citation rate: share of answers that link to your website as a source (see AI citations)
  • Share of model: your share of all brand mentions compared with competitors (see Share of model)
  • Sentiment: whether the portrayal is positive, neutral or negative
  • Referral traffic: visits via links from ChatGPT, Perplexity, Gemini or Copilot

Two limitations are worth knowing: AI answers vary from query to query, so individual samples say little. And Google Search Console does not report clicks from AI Overviews and AI Mode separately but counts them under the regular "Web" search type.

Common mistakes

  • Blocking all AI crawlers: if you also block the search crawlers (for example OAI-SearchBot or PerplexityBot), your pages cannot appear as a source in those systems. Most providers let you control training and search separately.
  • Relying on a single measure: creating an llms.txt is quick, but its effect has not been demonstrated. GEO results from many factors.
  • Manipulation: providers treat hidden instructions to AI systems in page text or mass-produced content without original value as spam. This can put visibility in search and AI answers at risk.
  • Neglecting classic SEO: without indexing, a sound technical setup and relevant content, the basis on which AI systems select their sources is missing.
  • Measuring only once: a single query in ChatGPT says little about actual visibility.

Limits of GEO

GEO is a young field. The systems change quickly, the selection criteria are not disclosed, and answers are not reproducible. In addition, a mention in an AI answer does not necessarily lead to a website visit, because many questions are already settled in the answer (see Zero-click search). Recommendations on GEO should therefore be understood as well-founded assumptions that you can verify with your own measurements.

Further terms

Sources

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