GEO (Generative Engine Optimization)
Similar to AEO, the technical and strategic process of improving visibility within generative AI models.
What is GEO?
Generative Engine Optimization is the practice of increasing how often and how favourably a brand, product or page appears in the output of generative AI systems. The term entered common use through academic work that studied how changes to source content affect its inclusion in generated answers, and it is now used broadly for the whole discipline of AI search visibility.
GEO and AEO are frequently used interchangeably, and most practitioners treat them as the same field. Where a distinction is drawn, AEO tends to emphasise being the direct answer to a question, while GEO emphasises influencing generative outputs more generally, including recommendations, comparisons and summaries where the brand is one of several mentioned. Both rest on the same foundations: accessible content, answer-shaped structure, verifiable specifics, strong entity signals and off-site presence on the sources engines retrieve.
GEO work is typically organised as a loop. Define the prompts that matter for the business, measure current visibility per engine, analyse the sources engines cite, adjust on-site content and pursue off-site placements, and re-measure. Because generative engines update their models and retrieval sources regularly, the loop never really closes.
Why it matters for AI search
Generative engines are becoming a primary research surface for buyers, and the sources they rely on differ from classic search rankings. A brand can rank well in Google and still be absent from ChatGPT or Perplexity answers. GEO gives teams a way to understand that gap, close it, and prove the impact with mention, citation and share-of-voice metrics rather than assumptions.
Related terms
AEO (Answer Engine Optimization)
The practice of optimizing content to be cited and recommended by AI-driven search engines and conversational assistants like ChatGPT and Perplexity.
Ask Engine Optimization
A synonym for Answer Engine Optimization, focusing on a user's direct 'ask' rather than a search query.
LLM (Large Language Model)
A type of AI trained on vast amounts of text data to understand and generate human-like language (e.g., GPT-4, Claude 3).
RAG (Retrieval-Augmented Generation)
A technique that allows LLMs to pull in real-time information from external sources (like a website) before generating an answer.
Frequently asked questions
Is GEO the same as AEO?+
Practically, yes. Both describe optimising for visibility in AI-generated answers. Some people use AEO for direct question answering and GEO for broader generative outputs, but the tactics and metrics are the same.
What are the core GEO tactics?+
Allow AI retrieval crawlers, render content server-side, open pages with a direct answer, add structured data and an llms.txt file, keep brand facts consistent across the web, earn mentions on the third-party sources engines cite, and track results across a fixed prompt set.
Can I measure GEO results?+
Yes. Track mention frequency, citation share, AI share of voice and sentiment across a defined set of prompts on each engine, on a recurring schedule, and compare against competitors.