LLM optimization is the work of shaping a website and brand footprint so AI answer engines such as ChatGPT, Gemini, and Perplexity cite the brand when they answer a buyer's question.
The term can be a little misleading, since you're not changing the model itself. Instead, you're making decisions about your content to make it more appealing to these new answer engines. But since it's the phrase that's most commonly associated, it's the one we'll use!
What is LLM optimization
LLM optimization is the practice of increasing how often a large language model's answers mention and cite your brand. LLM means large language model, and ChatGPT, Claude, Gemini, and Google's AI Overviews all run on one.
Practitioners also call this work LLMO or GEO. Whatever the label, it treats the model as a retrieval surface you don't control. The engine picks a handful of sources and writes an answer that may or may not name you, and the default outcome is silence.
In one 2026 analysis of 177 brands across eight AI platforms, only 18 brands had a mention rate above zero. We call this practice AI visibility because it names the outcome you measure rather than a technique set that keeps changing.
LLMO, GEO, and AEO compared
Only one of these acronyms has a peer-reviewed definition. Generative engine optimization comes from an IIT Delhi and Princeton paper posted in November 2023 and published at KDD '24, which defines GEO as rewriting a page's presentation, style, and content to raise its visibility in answers an LLM synthesizes from multiple sources. The paper's three best-performing methods, adding citations, quotations, and statistics, improved its visibility metric 30% to 40% over unoptimized content.
The other terms have industry origins and no canonical definition. LLMO is a practitioner coinage, and in academic networking papers the same acronym means an LLM optimizer, a system that uses language models as optimization agents. AEO dates to a 2017 Trustpilot white paper and originally meant featured snippets and direct-answer boxes.
Loosely, AIO is the umbrella label, GEO and practitioner LLMO are near-synonyms beneath it, and AEO is the narrowest. Trade-press writers doubt any one term will stick, and we agree. Whatever you call it, treat the answer engines as a retrieval surface rather than a publishing channel. In our experience each engine has its own citation habits, overlap with Google's top ten is smaller than most SEO teams expect, and fresh pages with quotable statistics and named sources are what gets picked.
Technical setup for AI answer engines
Start with robots.txt. Vendors run separate crawler tokens for training and for search, and blocking the wrong one removes you from answers while leaving you in training data. OpenAI's crawler docs name GPTBot for training and OAI-SearchBot for ChatGPT search. Anthropic runs ClaudeBot for training and Claude-SearchBot for search, and Perplexity indexes with PerplexityBot.
To stay in AI Overviews while keeping your content out of Gemini training, disallow Google-Extended and leave Googlebot alone. Then check your CDN, since Cloudflare now blocks known AI crawlers by default for new customers and an edge rule can override a permissive robots.txt.
llms.txt is a Markdown file at /llms.txt that summarizes a site for language models. Google says outright that no new machine-readable files or special markup are needed to appear in its search or generative features, and none of the other vendors has confirmed that its public-web crawler reads third-party llms.txt files. The file takes twenty minutes, so ship it, and keep it out of the forecast.
Schema.org markup deserves the same expectations. Mark up Article and Organization for Google's rich results and entity consistency, but the independent citation studies we've read come back null for schema once domain-level trust is controlled for.
How to measure results
Visibility measurement fails when a single number, sampled once, gets treated as a fact. These metrics are unstable by construction and have to be sampled like a poll. Track five:
- Presence: does the brand appear at all in the answers your buyers read.
- Citation rate: is your URL used as a source.
- Reputation and perception: how the brand is characterized and framed.
- Share of model voice: brand appearances ÷ total answers across a prompt set.
- Influence: how much the brand shapes the category narrative.
Four rules for collecting them:
- Separate mentions from citations. A mention is your brand named in the answer text. A citation means the engine used your URL as a source.
- Sample across days. A study that queried four AI search engines daily for two months found source sets overlapping only 34% to 42% between consecutive days. One daily query tells you almost nothing.
- Repeat each prompt. Brand lists change between identical runs, so ranking position inside an answer is meaningless. Visibility percentage over many prompts and repetitions is defensible.
- Maintain a standing panel. A few hundred high-intent prompts, run monthly across at least three engines. Report ranges, and expect platform drift to move the number as often as your own work does.
Run that panel manually first. CheckThat, the AI visibility platform we operate, benchmarks nearly 200 B2B software categories, 5,800+ brands, and 2.6M+ AI responses, which gives you a category baseline to compare against, and it feeds the Presence, Reputation, Perception, and Influence scores in the GrowthX platform.
Common mistakes and when not to optimize
Treating LLMO as renamed SEO is the most common mistake. Search fundamentals still matter, because rank and domain-level trust are the strongest predictors of being cited at all, and ranking well still doesn't guarantee a citation, because engines also draw on off-site mentions and entity associations. So keep the SEO fundamentals, drop the checklist of AI-specific markup, and spend the freed time on quotable, current pages and on the third-party mentions that build domain-level trust.
There's also a case for not optimizing yet. If you're one of the 159 brands in that 177-brand sample with zero mentions, the first job is a prompt panel that confirms the zero and shows which competitors the engines name instead. If maintaining that panel across separate tools becomes the bottleneck, book a demo and we'll show you how the GrowthX platform runs the loop in one workspace.
