LLM optimization (LLMO).
Engineering content for the model, not just the surface.

Definition

What LLMO actually is.

LLM optimization (LLMO) is the practice of engineering content so that large language models — the underlying technology behind AI assistants like ChatGPT, Claude, Gemini, and Perplexity — cite the content when synthesizing responses. The "LLM" framing emphasizes the technical mechanism: LLMs are the systems doing the citation, and content has to be structured for LLM ingestion specifically.

The term overlaps with answer engine optimization (AEO) and generative engine optimization (GEO). The three terms describe the same practice; LLMO is preferred by technical practitioners and engineers, AEO by content marketing teams, GEO as a middle-ground framing.

Why it matters

The paragraph gets written with or without you.

When a buyer asks an assistant to compare vendors in your category, the assistant writes a paragraph. That paragraph names some brands, leaves others out, and describes each one in a sentence or two. It is often the first description of your company a buyer reads, and it is assembled from whatever the model found, not from what you would have said.

Three things make that paragraph different from a search result. First, there is no list of ten links to scroll past: the buyer reads the answer, and the brands left out of it are simply not considered. Second, the description is a synthesis, so an outdated pricing page or a two-year-old forum thread can shape it as much as your homepage. Third, many answers end the session. The buyer gets what they needed and never visits a site, so the usual traffic reports show nothing at all.

That last point is why LLMO is a visibility discipline before it is a traffic discipline. The goal is to be named, described accurately, and cited as a source. Clicks follow for some queries and not for others; the description follows for all of them.

The mechanics

How LLMs actually cite content.

LLMs are trained on large corpora of text and (for assistants with web search enabled) augmented with live-retrieved content at query time. When asked about a brand, the LLM identifies the brand as a named entity, retrieves the highest-confidence facts associated with it across training and retrieval data, and synthesizes a response paragraph.

LLMO engineers content so that the LLM finds high-quality, structurally-extractable, entity-rich claims about your brand from authoritative sources. Four practical levers:

• →Semantic chunks (~80-120 words) — content the LLM can extract verbatim without losing context.
• →Entity-rich writing — explicit naming so the LLM identifies your brand as a topic-relevant entity.
• →Dense schema markup — explicit labeling so the LLM doesn't have to infer page structure.
• →Pillar + cluster architecture — topical authority signal that compounds across many cluster pages.

For per-LLM detail see how ChatGPT cites brands, how Claude cites sources, how Perplexity works, and how Gemini cites sources.

How to do LLMO

Six steps, in the order they pay off.

LLMO work splits into what you control on your own site and what other sites say about you. Start on-site, where you control the timeline, then work off-site, where you don't. The sequence below is the short version; the full build plan, from prompt panel to monthly re-measurement, is in how to optimize for AI citation.

1. 1. Write down the questions you need to win.

   Before touching a page, list the questions a buyer actually asks an assistant: "best tools for X", "X vs Y", "is X worth it for a team of ten". Pull them from sales calls, support tickets and the comparisons buyers mention. Keep the list fixed. A stable question set is what makes every later step measurable.

2. 2. Record what the assistants say today.

   Ask each question in each assistant and save the verbatim answers. Note whether you appear, where in the paragraph, how you are described, and which sources are cited. This baseline tells you whether your problem is absence, misdescription, or a competitor owning the citations. The AI visibility audit guide walks through it in ninety minutes.

3. 3. Fix the page that defines you.

   Make sure one page on your site states plainly what you are, who it is for, and how you differ, in a passage short enough to be lifted whole. Assistants can only repeat a clear description if one exists. If your homepage leads with a slogan, the model has to infer your category from everything else it reads.

4. 4. Build the pages your questions need.

   Each question from step 1 should have a page that answers it directly: a category explainer, a comparison, a use-case page. Answer first, then support the answer. This is the pillar and cluster work described above, aimed at the specific questions you recorded rather than at keywords in general.

5. 5. Work on what other sites say.

   For comparison questions, a brand describing itself is the least neutral source available, so independent descriptions of you matter. Off-site work is covered in its own section below.

6. 6. Re-measure on a fixed schedule.

   Run the same questions monthly and compare against the baseline. Expect slow movement: models and their retrieval sources update on their own schedules, so weekly checks mostly measure noise.

On-site content tactics

Write passages a model can lift whole.

The four levers above (chunks, entities, schema, architecture) describe structure. These tactics describe the writing inside that structure.

• →Answer first. Put the direct answer in the opening sentence of each section, then the reasoning. A passage that only makes sense after three paragraphs of setup is hard to quote.
• →Publish things only you know. Original data, a documented method, a real customer outcome with permission to name it. Content that restates what every other page says gives a model no reason to cite you over them.
• →Be specific. Concrete facts (prices, limits, supported integrations, dates) are easier to repeat accurately than adjectives. Every specific claim should be one you would be comfortable seeing quoted back without context.
• →Name things consistently. Use the same product name, category name and descriptions across pages, so the model connects them to one entity instead of several.
• →Keep it current. Retire or update pages that describe old pricing, discontinued features or past positioning. A model has no way to know which of your pages you consider out of date.

Off-site work

Other sites describe you too.

Your own pages tell a model what you say about yourself. Other sites tell it what everyone else says. Four channels are worth the effort, and each has rules worth respecting.

Technical access

A model can't cite what it can't fetch.

Start with ordinary search hygiene. Google states that there are no additional requirements to appear in AI Overviews or AI Mode: a page must be indexed and eligible to show in Google Search with a snippet. If a page is not in the index, it is not a candidate.

Then check your robots.txt against the AI crawlers, because the vendors separate search from training. OpenAI documents OAI-SearchBot and GPTBot as independent settings, so you can allow search while opting out of training. Anthropic lists ClaudeBot, Claude-SearchBot and Claude-User for training, search and user-requested fetches. Google says Google-Extended does not affect inclusion or ranking in Google Search. A blanket block meant for training can quietly remove you from AI search answers.

Finally, make sure the content is in the HTML, not only rendered by JavaScript after load, and consider an llms.txt file: a proposed convention, not a requirement, for pointing models to your most useful pages. See what llms.txt is and what it does.

Measuring results

Measure the paragraph, not the clicks.

Because many AI answers end without a visit, traffic reports undercount LLMO results. Measure the answers directly, using the fixed question set from step 1:

• →Presence: the share of questions where you are named at all, per assistant.
• →Position and description: where in the paragraph you appear, and whether the description is accurate.
• →Sources: which pages are cited, yours or third parties', and whether a competitor's page is being used to describe you.
• →Branded vs unbranded: track questions that name you separately from category questions that don't. The second group is the one LLMO is trying to move.

Answers vary from one run to the next, so ask each question more than once and compare rates over months rather than single answers. For the full method see how to measure AI visibility and what AI visibility is.

LLMO vs prompt engineering

Two disciplines, often confused.

LLMO optimizes content on the web so LLMs cite it. Prompt engineering optimizes the prompts users send to LLMs so the LLM produces better task-specific responses. They're complementary but distinct.

LLMO is brand-visibility work done by content teams and AI visibility specialists. Prompt engineering is task-performance work done by users and application developers. A brand can win LLMO without ever doing prompt engineering; a user can master prompt engineering without affecting any brand's LLMO position. The terms are sometimes conflated by audiences unfamiliar with either; in practice they sit at different layers of the AI stack.

Frequently asked questions