LLM Optimization: How to Optimise Content for Large Language Models

People now ask large language models the questions they used to type into Google. ChatGPT crossed one billion monthly active users in June 2026, having passed 900 million weekly active users in February, according to OpenAI usage figures compiled by DemandSage. An analysis of 1.5 million conversations found that roughly half were people asking questions and another 40 percent were people getting work done. That is a meaningful share of the demand that used to flow through a search box, and it now resolves inside a generated answer rather than a list of links. LLM optimization is the practice of structuring and improving your content so large language models can understand it, trust it, retrieve it and reference it when they write those answers. It is a layer on top of strong traditional SEO, not a replacement for it.

What LLM optimization actually means

LLM optimization, sometimes shortened to LLMO, covers everything you do to make your content legible and citable to large language models. The reason it is worth treating as its own discipline is that an LLM does not read the web the way a person or a classic search crawler does. It pulls from two different places. The first is parametric knowledge, meaning facts baked into the model during training, which is why an LLM can answer many questions with no live lookup at all. The second is retrieval, where the model runs a live web search at query time and grounds its answer in the pages it finds, an approach often described as retrieval augmented generation. IBM's explainer on RAG describes this retrieval layer as the way models supplement their training data with current, authoritative sources. Optimising for LLMs means earning your way into both paths: being well represented in the open data the model learned from, and being the kind of page the model reaches for when it searches live.

The model usually makes a binary decision first: answer from memory, or go and search. When it searches, the surfaces it touches are wider than classic search results. The same optimisation work that helps you appear in an AI answer also affects whether an AI agent recommends your tool, or whether an enterprise assistant built on a general model surfaces your brand. That breadth is what separates LLM optimization from the narrower search-focused disciplines it overlaps with, and it is why the tactics reward clarity and credibility over keyword density.

How an LLM decides what to cite

When a model chooses to search, it runs a pipeline that looks broadly like this: retrieve candidate pages from a search index, rank them by relevance and structure, extract the specific facts that answer the question, and attribute those facts back to a source. Rankio's breakdown of how LLM citations work describes why pages with direct answers, clean headings, tables and structured data get cited more often: they are simply easier to extract from and easier to attribute confidently. A model that can lift a clean, self-contained statement from your page and trust where it came from will cite you. A model that has to untangle a long, meandering section to find the same fact will often skip you for a clearer source.

The unit that matters here is the chunk, not the page. Models extract passages of roughly 100 to 300 tokens that answer a sub-question, as Hubstic's guide to LLM SEO explains. That has a direct writing consequence: every paragraph should stand on its own. If understanding one passage requires three paragraphs of earlier context, the chunk loses its meaning the moment it is pulled out, and the model is less likely to use it. Leading with a direct answer and then explaining it, rather than building slowly to a conclusion, is one of the highest-leverage habits in LLM optimization for exactly this reason.

LLM optimization, GEO and AEO: how they relate

If you have read about generative engine optimization (GEO) or answer engine optimization (AEO), you have already met close cousins of LLM optimization. The honest summary is that the underlying content patterns overlap heavily, and the differences are mostly about scope and emphasis. LLMO is the broadest umbrella. GEO focuses specifically on being included in the synthesised response a generative engine produces. AEO focuses on being selected as a cited source when an answer engine picks where its facts come from. Several practitioner breakdowns, including DevCommX and SEO Sherpa, frame the terms this way: same toolkit, different framing of the goal. You do not need to pick one label. You need to do the work that all three describe.

For a deeper treatment of the boundaries, our guide to SEO vs AEO vs GEO walks through where each one ends, and the dedicated pieces on generative engine optimization and answer engine optimization go further into each. The practical takeaway is that optimising well for LLMs covers most of what GEO and AEO ask for, plus the non-search surfaces the other two do not reach.

The tactics that actually move LLM visibility

The strongest evidence we have for what works comes from a peer-reviewed paper. In their GEO study presented at KDD 2024, researchers from Princeton and IIT Delhi tested nine content strategies and found that the best of them could lift visibility in generative engine responses by up to 40 percent. The standout winners were adding relevant statistics, including quotations, and citing sources. Keyword stuffing, the reflex left over from old SEO, did not work. The authors are clear that efficacy varies by domain, so treat the headline number as a directional finding rather than a guarantee, but the direction is consistent: content that is denser with verifiable, attributable substance gets referenced more. The paper is available on arXiv. Secondary analyses have pushed the framing further, with one summary from Stackmatix reporting that pages around position five saw a 115 percent visibility lift, though that figure comes from the analysis rather than the original paper and should be read as a secondary estimate.

That research lines up neatly with a more durable tactic: publish things only you can publish. Original survey data, benchmarks, customer outcomes and proprietary numbers make your page structurally necessary for a complete answer, because the model cannot assemble the same fact from anywhere else. Hubstic makes this point well, and it is the most defensible position a brand can hold, since it cannot be copied by a competitor reading the same advice. Underneath the content, structure does the heavy lifting. A clean H1, H2 and H3 hierarchy, self-contained paragraphs, a direct answer before the explanation, and the occasional table or list all make extraction easier.

Crawlability is the unglamorous prerequisite. AI crawlers read raw HTML and generally do not execute JavaScript, so content hidden behind tabs, accordions or client-side rendering can be invisible to them, as Hubstic notes. If a model cannot see the text, none of the other tactics matter. Schema markup sits in a more contested place. Structured data gives machines a clean, labelled layer of facts tied to known entities, which can help a model verify rather than infer, and WitsCode's LLM SEO guide frames it that way. But its direct causal effect on AI citations is debated, and as we cover in does schema markup help with AI visibility, it is best treated as a clarity aid rather than a guaranteed lever. The same honesty applies to llms.txt: only around 10 percent of domains have adopted it, and no major AI company has publicly confirmed reading it in production as of early 2026, per SeekLab's 2026 guide. It is low cost and reasonable to add, but unproven, and our explainer on what llms.txt is lays out the evidence in full.

Finally, much of LLM visibility is earned off your own site. Third-party validation moves the needle hard: reviews on G2 and Trustpilot, authoritative roundups, and mentions on Reddit are all signals models lean on when deciding which brands to name. Our practical guide on how to get cited by AI pulls these levers together. The short version is that being mentioned credibly across the wider web often matters more than any single change to your homepage.

Why you cannot optimize what you cannot measure

Here is the part most LLM optimization advice skips. LLM answers are highly inconsistent, which makes a single spot-check almost worthless as a measure of progress. In research published in January 2026, SparkToro and Gumshoe.ai ran 12 prompts through ChatGPT, Claude and Google AI Overviews nearly 3,000 times across 600 volunteers. They found less than a one in 100 chance that two responses would return the same brand list, and roughly one in 1,000 for identical ordering. Their conclusion was blunt: ranking-position metrics for AI are unreliable, and even visibility percentages only hold up when measured across many runs. A single query tells you what one model said once, not what it tends to say.

The variance compounds across platforms. Search Engine Land's prompt-tracking guide reports that citation volumes for the same brand can differ by up to 615 times between platforms, and that only around 12 percent of cited sources overlap across ChatGPT, Perplexity and Google AI. So a tactic that lifts you in one engine may do nothing in another. This is the case for monitoring rather than checking, and it is the soft Honeyb point: you cannot improve LLM visibility you cannot measure, and the sheer variance is exactly why daily, multi-run, multi-engine tracking beats an occasional manual look. We made the full argument in why spot-checking your AI visibility doesn't work. Measurement is not the last step in LLM optimization; it is the feedback loop that tells you whether any of the work landed.

Where to start

If you are starting from zero, sequence it. First, make sure AI crawlers can actually read your key pages by serving the important content in raw HTML rather than behind client-side rendering. Second, restructure your highest-intent pages so each one leads with a direct answer and breaks into self-contained, extractable chunks. Third, add the substance the research rewards: real statistics, named quotations and cited sources, and wherever possible original data only you can publish. Fourth, build off-site credibility through reviews, roundups and genuine community presence. Treat schema and llms.txt as low-cost housekeeping rather than silver bullets. Then measure across engines over time, because that is the only way to tell signal from noise. LLM optimization is not a one-time project. It is the ongoing work of staying legible, credible and present in the answers your customers now read first.