Someone asks ChatGPT which SEO consultant to hire in Reading. Your website either gets mentioned or it doesn't. Bing Copilot explains what topical authority is — a generative engine in action. It cites your content or a competitor's. LLM optimisation is the practice of ensuring it's yours. It is happening now. Most businesses have no strategy for it.
What is LLM Optimisation?
What is LLM optimisation? LLM optimisation (Large Language Model optimisation) is the practice of structuring content, building entity authority, and establishing digital presence in ways that increase the likelihood of AI language models selecting your content as a source. It applies to ChatGPT, Bing Copilot, Google Gemini, Perplexity, Claude, and any AI system that retrieves and synthesises web content.
How Do LLMs Decide What to Include in Their Answers?
Large language models generate answers through two mechanisms. First, their training data — the content ingested during model training, which shapes baseline knowledge. Second, retrieval-augmented generation (RAG) — real-time retrieval of web content to provide current, cited information within answers.
The retrieval layer is the primary opportunity for businesses. LLMs with web access (Bing Copilot, Perplexity, ChatGPT with browsing) retrieve content in real time. Content that is findable, trustworthy, and clearly structured gets retrieved and cited. Content that is technically inaccessible, thin, or poorly structured gets passed over — regardless of how good it actually is.
How Does LLM Optimisation Differ from Traditional SEO?
Traditional SEO optimises for ranking positions in a list. LLM optimisation optimises for citation selection from a retrieved pool. The differences in practice:
Answer directness — LLMs extract the most direct answer available. Content burying its conclusion after three paragraphs of context loses to content answering in the first sentence. Traditional SEO tolerates this structure. LLM optimisation penalises it.
Entity recognition — LLMs have higher confidence in entities they've encountered frequently in credible contexts. A business with strong entity SEO signals (consistent structured data, mentions from established sources, a clear Knowledge Graph presence) receives citation preference over weakly-established entities with equivalent content.
Source credibility signals — LLMs weight sources based on domain authority patterns, citation frequency in training data, and structured trustworthiness signals. Building genuine authority — through comprehensive content, real expertise signals, and external recognition — matters for LLM visibility in ways that superficial SEO tactics don't address.
What Makes Content LLM-Ready?
Content optimised for LLM citation shares specific characteristics. My guide on optimising content for AI search covers the 7 key content patterns in detail. Build these into every piece you publish.
Factual precision with specific data. LLMs prefer sources making specific, verifiable claims. "Traffic increased by 280% in 8 months" outperforms "traffic improved significantly" as a citable fact. Include specific numbers, named sources, and verifiable claims throughout your content.
Direct question-answer structure. Format content so questions appear as headings and answers follow immediately in the first sentence of each section. LLMs extract clean Q&A structures reliably. Narrative content with embedded answers is extracted less accurately.
Comprehensive topic coverage. LLMs prefer sources addressing multiple facets of a topic. One comprehensive guide covering five related questions outperforms five separate pages each covering one question. The model extracts multiple pieces of information from one trusted source. This is why topical authority and LLM visibility are closely linked.
Clean technical implementation. LLM retrieval systems cannot access technically inaccessible content. Fast load times, clean HTML, and proper crawl access matter for LLM retrieval as much as they matter for traditional search engine indexation.
How Do You Track LLM Visibility?
Current LLM visibility tracking is imperfect but improving. Bing Webmaster Tools provides Copilot citation data — which pages are cited in Copilot responses and for which queries. This is the most reliable current data source. Manual query testing across target topics in ChatGPT and Perplexity reveals current citation patterns for those platforms.
Across my portfolio tracking: pages earning AI citations consistently share specific content characteristics — direct answer formatting, specific data points, and entity-establishing context. Identify which content gets cited. Replicate those patterns across your site. That is the practical LLM optimisation process.
Is LLM Optimisation Worth Investing In Now?
AI search currently handles 15–25% of queries. That share grows. Businesses investing in LLM optimisation now establish entity recognition in AI systems before competitors do. Waiting until AI search reaches 50% of queries means catching up to established citation patterns — not getting ahead of them.
Related Articles
- Optimise Content for AI Search — the 7 content patterns that earn AI citations
- Generative Engine Optimisation (GEO) — how GEO and LLM optimisation work together
- What is Entity SEO? — entity credibility as the foundation for LLM visibility
- How to Build Topical Authority — topical depth is what makes content LLM-worthy
I track AI citation data across a 44-site portfolio and can identify the specific patterns earning citations in your niche. Contact me for a free consultation.