LLM optimization (LLMO) is the practice of improving how a brand, product or website shows up in answers from tools powered by large language models, such as ChatGPT, Gemini, Claude, Perplexity and Microsoft Copilot. The goal is to be mentioned accurately, recommended when relevant and cited as a source.
For businesses, LLMO matters because more research now happens inside AI assistants. If those tools describe your category without naming you, or describe you incorrectly, you lose ground before a buyer ever reaches your site.
What is LLM optimization?
LLM optimization is an umbrella term for the work of making a brand easy for AI systems to find, understand, trust and cite. It covers your own website, what others publish about you and how consistent your key facts are across the web.
The term overlaps heavily with generative engine optimization (GEO) and answer engine optimization (AEO). Different agencies and tool vendors prefer different labels, but the underlying work is largely the same. LLMO puts the focus on the model itself, the large language model that generates the answer, rather than on the search interface around it.
Why LLM optimization matters
AI assistants are a real discovery channel. OpenAI said ChatGPT reached 900 million weekly active users in February 2026. Google's AI Overviews and AI Mode put generated answers directly in search results. Buyers increasingly meet a brand for the first time inside an AI answer.
Content changes can affect AI visibility. The GEO research paper by Aggarwal and colleagues, presented at KDD 2024, found that certain changes could boost a source's visibility in generative engine responses by up to 40% in its tests. Adding quotations, statistics and source citations performed best, while keyword stuffing scored below the unoptimized baseline.
Accuracy is at stake. AI tools can repeat outdated or wrong information. Clear, consistent facts reduce the chance of an AI hallucination about your prices, services or locations.
How LLM optimization works
Two ways a model learns about you
LLMs draw on two kinds of information. The first is training data, the large body of text a model learned from before its knowledge cutoff. The second is retrieval: many assistants search the web or an index at answer time and ground their responses in what they find. LLMO addresses both.
Optimizing for retrieval
Retrieval is where most practical work happens, because it reflects changes faster. Pages need to be crawlable by the bots that power AI search, indexed by search engines and structured so a passage can be understood on its own. For Google's AI features, Google's AI features documentation says there are no additional requirements or special optimizations beyond standard SEO best practices. A page must be indexed and eligible to show a snippet to appear as a supporting link.
Building a public footprint
Models and retrieval systems both learn from what others say about you. Reviews, press coverage, directory listings, community discussions and expert mentions all shape how AI describes a brand. Consistent naming and structured data help systems connect those mentions to the right entity.
Measuring the results
LLMO is measured by testing. Teams run realistic prompts through AI tools and track mentions, citations, accuracy and competitors over time, rather than relying on a single answer.
LLM optimization best practices
- Fix the fundamentals first. Check crawler access in robots.txt, indexing in Search Console and Bing Webmaster Tools, and whether key content loads in the initial HTML.
- Answer real questions clearly. Use descriptive headings and put direct, self-contained answers near the top of each section.
- Back up claims. Add specific data, named sources and quotes from real experts. This matches what performed well in the GEO research.
- Keep facts consistent. Make sure your name, description, pricing and locations match across your site, profiles and listings.
- Earn independent mentions. Coverage on relevant, trusted sites gives AI systems more sources that confirm who you are and what you do.
- Test, then adjust. Run the same prompt set regularly and look for patterns before changing course.
Common mistakes and misconceptions
"LLMO replaces SEO." AI search features rely heavily on search indexes and web pages. Google says standard SEO best practices still apply to its AI features, so SEO remains the base that LLMO builds on.
"Hidden instructions can make AI recommend us." Placing hidden text or instructions on a page to steer AI answers is risky. Google's spam policies include attempting to manipulate generative AI responses in Google Search, and they also cover hidden text.
"You need special files to be visible." Google says you don't need new machine-readable files, AI text files or special markup to appear in its AI features. Files like llms.txt are optional extras, not requirements.
"We can get into the training data quickly." Training happens on the model provider's schedule, and no one can place a brand into a model on demand. Retrieval-based visibility is where faster progress is possible.
"It's a one-time project." Models, search indexes and competitors keep changing. LLMO works best as an ongoing routine.
LLMO vs. GEO vs. AEO
These three terms describe overlapping work. GEO, named in the 2023 research paper, focuses on generative engines that combine search with generated answers. AEO grew out of optimizing for direct answers like featured snippets and voice assistants. LLMO emphasizes the language model behind the answer. In practice, most teams use one of these labels to describe the same mix of SEO fundamentals, clear content, entity consistency and off-site reputation.
How Butter approaches LLM optimization
At Butter, we call this work GEO, and it's the core of what we do. We treat it as an extension of solid SEO rather than a replacement. Our founder, Mo Bozo, spent 11 years growing a SaaS marketing program and later built and sold an SEO agency, so we start with fundamentals.
Our GEO plan includes monthly AI prompt testing, where we check how AI tools answer the questions your buyers actually ask, plus an indexing strategy to make sure the right crawlers can reach you. Each month we deliver one AI-crawlable content citation and one backlink, along with knowledge graph submissions, schema markup guidance and on-page and off-page recommendations. We won't promise placement in any AI answer, because nobody honestly can. Our GEO services page lays out how it fits together.
Frequently asked questions
Is LLMO the same as GEO?
Mostly, yes. LLMO, GEO and AEO describe overlapping work aimed at visibility in AI-generated answers. The labels differ in emphasis, but the core tactics, such as crawlable pages, clear answers and consistent facts, are the same.
Does LLM optimization replace SEO?
No. AI search features depend on crawled and indexed web content, and Google says standard SEO best practices apply to its AI features. LLMO builds on SEO rather than replacing it.
How long does LLM optimization take to work?
It varies. Changes that affect retrieval, like fixing crawler access or improving a page, can show up in search-based AI answers relatively quickly. Changes to what a model learned in training depend on the provider's release schedule.
How do I measure LLM optimization?
Run a consistent set of realistic prompts through the AI tools your buyers use, repeat them over time and track mentions, citations, accuracy and competitors. Pair that with referral traffic data from your analytics.