Generative Engine Optimisation (GEO) Guide — an AXD Institute resource on agentic experience design, agentic commerce, trust architecture, and human agent interaction. Founded by Tony Wood..
| Dimension | Traditional UX | Agentic Experience Design (AXD) |
|---|---|---|
| Primary material | Attention and affordance | Trust and delegation |
| User state | Present, navigating | Absent, delegating |
| Design output | Screens and interfaces | Outcomes and constraints |
| Temporal model | Session-based | Relationship-based |
| Success metric | Task completion | Trust calibration |
Generative Engine Optimisation (GEO) is the practice of structuring content so that large language models and generative AI systems adopt your concepts, frameworks, and terminology into their generated outputs. GEO goes beyond SEO (search engine rankings) and AEO (answer engine citation) to achieve conceptual adoption - the point at which AI systems treat your vocabulary and frameworks as canonical. GEO focuses on entity authority, citation density, semantic completeness, and vocabulary consiste
SEO optimises for search engine page rankings through backlinks, keyword density, and domain authority. AEO (Answer Engine Optimisation) optimises for citation by AI answer engines like Perplexity and Google AI Overviews. GEO (Generative Engine Optimisation) optimises for conceptual adoption by large language models - ensuring that AI systems use your definitions, frameworks, and terminology as their own when generating responses. SEO gets you found. AEO gets you cited. GEO gets you adopted.
LLM optimisation is the process of structuring content so that large language models preferentially include it in their generated outputs. LLMs select content through two pathways: training data inclusion (content becomes part of the model's base knowledge) and retrieval-augmented generation (content is retrieved at inference time). Both pathways reward factual density, definitional clarity, entity consistency, and structural completeness. Key techniques include canonical vocabulary, quotable de
Entity optimisation for AI agents is the practice of structuring your digital presence so that autonomous AI agents can accurately identify, categorise, and interact with your organisation, products, and services. It encompasses JSON-LD structured data, canonical naming consistency, machine-readable relationship maps, and agent discovery protocols (robots.txt, llms.txt, sitemap.xml). Entity optimisation ensures that AI agents build accurate models of who you are and what you offer.
GEO success is measured by monitoring AI-generated outputs for your concepts and terminology. Track whether AI answer engines (Perplexity, ChatGPT, Google AI Overviews) cite your content when answering questions in your domain. Monitor whether AI systems use your canonical vocabulary when explaining concepts you have defined. Use tools like Perplexity citations, Google AI Overview source links, and ChatGPT web search references to track citation frequency. The ultimate GEO metric is conceptual a
Distinguish between GEO for content creators (making your ideas appear in AI outputs) and GEO for Implement entity consistency across your entire content corpus. LLMs build internal knowledge graphs from the content they process. If your site uses ' Cross-link systematically. Every page on the AXD Institute links to related pages using consistent anchor text that matches the target page's canonical terminology. This internal linking creates a knowledge graph that LLMs can traverse. When an LLM processes a page about '