AI Search
LLM Optimisation: Structuring Content for Large Language Model Retrieval
LLM optimisation is the practice of structuring a website's content, entity data, and accessibility in ways that make it easier for large language models to retrieve, interpret, and cite accurately. It applies to AI systems that use retrieval-augmented generation (RAG) to supplement their responses with real-time or indexed web content, including ChatGPT with browsing, Perplexity, and Google Gemini.
Why llm optimisation matters for UK businesses
Large language models do not read websites the way humans do. They retrieve content through crawlers that may not execute JavaScript, parse natural language into structured facts, and assess source authority based on signals including structured data, entity consistency, and content clarity. A page optimised for human readers may still perform poorly for LLM retrieval if its key facts are buried in dense prose or dependent on JavaScript to render.
LLM optimisation is increasingly relevant as AI models are used to answer commercial queries. A business whose website provides clear, directly quotable answers about what it does, who it serves, and what its results have been is more likely to be retrieved and cited accurately than one whose website uses vague marketing language and buries facts in long paragraphs.
How Khamare Clarke applies llm optimisation
LLM optimisation work covers: writing definitions and factual claims in the first sentence of each section (the most retrievable position), using structured headings that match the questions LLMs are likely to be answering, implementing FAQ schema to make Q&A pairs explicitly machine-readable, maintaining a llms.txt file as a plain-text structured overview for AI crawlers, and ensuring all pages render their content as static HTML without JavaScript dependency.
Entity consistency is particularly important for LLM retrieval. If a business is referred to by different names, in different formats, with different contact details across different pages, an LLM may retrieve inconsistent or contradictory information. Canonical entity schema with a stable @id resolves this by giving the LLM a single authoritative record to associate all page content with.
Is LLM optimisation the same as AEO?
They overlap significantly but are not identical. AEO is the broader practice of optimising for answer engine results. LLM optimisation specifically addresses the technical and content requirements for retrieval by large language model systems. All LLM optimisation is AEO work, but AEO also covers featured snippets and structured answer formats in traditional search engines that do not use LLMs. In practice, the two are addressed together.
Do LLMs crawl websites themselves?
Some do, through retrieval-augmented generation (RAG) systems that use web crawlers to gather real-time information before generating responses. ChatGPT's browsing feature, Perplexity, and Google's AI Overviews all retrieve live or recently crawled web content as part of their answer generation process. Other LLM-based products rely on content from their training data rather than live retrieval. Optimising for live retrieval requires attention to crawlability and content structure; optimising for training data incorporation is a longer-term process dependent on content being indexed and authoritative before each training cycle.
What format of content is easiest for LLMs to retrieve?
LLMs retrieve well-structured, clearly written content most reliably. The best-performing formats include: direct answers to specific questions in the first sentence, numbered or bulleted lists for multi-part information, short paragraphs with a single clear claim each, and tables for comparative data. Long paragraphs that contain multiple claims mixed with qualifications are harder to parse accurately. The writing style that works for LLM retrieval is also the writing style that works for human comprehension.
Apply LLM Optimisation to your business
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