LLM Optimisation (LLMO): How to Optimise for Large Language Models — 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 |
LLM Optimisation (LLMO) is the practice of ensuring that large language models - ChatGPT, Claude, Gemini, Perplexity, and others - accurately represent, cite, and recommend your brand in their generated outputs. LLMO addresses both training data influence (shaping how LLMs learn about your brand) and retrieval augmentation influence (ensuring your content is discovered and cited when LLMs search the web in real-time).
Optimise content for LLMs through five strategies: (1) definitional authority - provide clear, quotable definitions for key terms, (2) citation density - maximise specific, attributable claims, (3) entity consistency - use canonical terminology across all content, (4) structured data - implement comprehensive JSON-LD on every page, and (5) AI crawler access - allow LLM crawlers in robots.txt and deploy llms.txt for content discovery.
Traditional SEO optimises for search engine algorithms that rank pages based on relevance signals (backlinks, keyword density). LLM optimisation targets the internal knowledge representation of language models and their retrieval mechanisms. LLMs do not rank pages - they synthesise answers and cite authoritative sources. LLMO requires factual density, definitional clarity, entity consistency, and structured data rather than backlink profiles and keyword optimisation.
Measure LLM visibility through systematic testing: create 30-50 standardised queries, test across ChatGPT, Claude, Perplexity, Gemini, and Copilot monthly, and track three metrics: citation frequency (how often you are cited), knowledge accuracy (how accurately you are represented), and terminology adoption (how often your frameworks and terms are used in generated outputs).
As LLMs become the primary information interface for both humans and autonomous agents, brands invisible to LLMs lose access to a growing share of information-seeking interactions. LLM-mediated queries are growing at 40-60% annually while traditional search queries are plateauing. LLM optimisation ensures your brand is accurately represented, cited, and recommended when people and agents ask questions about your domain.
Implement entity consistency across all content. LLMs build internal knowledge graphs from the content they process. If your site uses different terms for the same concept across different pages, the LLM cannot build a coherent entity model. Use canonical terminology consistently - if you define '