LLM Optimization (LLMO)

What is LLM optimization in marketing?

LLM optimization (LLMO) is the practice of influencing how large language models mention, describe and cite a brand when they answer questions. It targets ChatGPT, Gemini, Claude, Perplexity and Google's AI features, and most published usage treats it as a synonym of GEO.

A large language model (LLM) is a neural language model trained on very large text collections that generates text in response to a prompt. ChatGPT, Gemini and Claude are products built on such models, and AI search engines such as Perplexity and Google AI Overviews use them to write answers. When a buyer asks one of them which supplier to choose, or what a company does, the model’s answer is the brand’s first impression. LLMO is the work of making that answer accurate and favourable.

Vendors define the term in similar words. Semrush, in a guide published on 5 January 2026, calls LLMO “a marketing tactic that aims to improve a brand’s visibility and portrayal in LLM-generated responses.” English Wikipedia does not give LLMO its own article; it lists large language model optimization among the other terms for generative engine optimization.

What sets the name apart is its focus. “LLM” points at the model rather than the search engine, and a model knows about brands in two ways — one of which involves no search at all.

How does a language model learn what to say about a brand?

A language model draws on 2 sources: facts absorbed from training data, and pages retrieved from the web when the question is asked. Answers without retrieval come from training data alone; one 2025 study found 24% of GPT-4o responses fetched no online content.

The two sources were named in the paper that introduced retrieval-augmented generation (RAG), Lewis et al. (arXiv 2005.11401, NeurIPS 2020). Parametric memory is the knowledge stored in the model’s weights during training. Non-parametric memory is an external index the model searches at answer time. Production AI assistants use both.

The training channel. Training data is the text a model learns from before release. What a model can say about a brand from memory is limited to what the pages in its training data said before its training cut-off. Changes through this channel arrive only when a new model version is trained, and the model cannot link to a source it learned from.

The retrieval channel. When the model decides a question needs current information, it searches the web through its operator’s search system, reads the retrieved pages and writes from them. Pages retrieved this way can be cited with a link, and changes to them reach answers once the page is recrawled.

The training channel is not a minor path. Strauss et al. (arXiv 2508.00838, August 2025) found that 24% of GPT-4o responses and 34% of Gemini responses were generated without fetching any online content. For those answers, whatever the model learned in training is the whole description of the brand.

Which LLMO techniques have published evidence?

Four LLMO techniques have published evidence: allowing crawler access, keeping pages indexable, adding cited sources, quotations and statistics, and structuring passages clearly. The 2023 GEO study measured 30–40% visibility gains from citations, quotations and statistics; keyword stuffing scored below baseline.

LLMO advice is abundant and evidence is not. The table grades each common technique by the kind of source behind it: an operator’s own documentation, a controlled study, or a vendor recommendation.

The table shows that the best-supported techniques act on the retrieval channel. The GEO paper (Aggarwal et al., first posted November 2023 and published at KDD 2024) rewrote one of five retrieved sources at a time and measured how much of the generated answer came from it. Adding cited sources, quotations and statistics raised visibility by 30–40% on its Position-Adjusted Word Count measure, while keyword stuffing scored below the unmodified baseline. Yu et al. measured a 17.3% rise in citation rate from structural content features across 6 engines.

The training channel has no comparable controlled evidence. Semrush’s first technique, “Get Brand Mentions on Commonly Cited Websites”, recommends finding the sites that LLMs frequently cite and securing brand mentions on them. No controlled study of its effect was located for this entry. It is listed here as a vendor recommendation, not as a measured result.

Control which AI crawlers train on and retrieve your pages

Set separate robots.txt rules for training crawlers and search crawlers. OpenAI's GPTBot collects training data while OAI-SearchBot surfaces pages in ChatGPT search, and the two settings are independent. Google-Extended limits Gemini training without affecting Google Search.

Each channel has its own crawlers, so each can be opened or closed on its own. The table groups the main ones by channel.

The split lets a site choose per channel. A publisher that wants to stay out of model training but remain citable in AI search can disallow GPTBot and ClaudeBot and allow OAI-SearchBot, Claude-SearchBot and PerplexityBot. OpenAI states that each of its settings is independent of the others, so blocking GPTBot does not remove a site from ChatGPT search.

# Out of training, open to AI search
User-agent: GPTBot
Disallow: /

User-agent: ClaudeBot
Disallow: /

User-agent: Google-Extended
Disallow: /

User-agent: OAI-SearchBot
Allow: /

User-agent: Claude-SearchBot
Allow: /

User-agent: PerplexityBot
Allow: /

Blocking training crawlers has a cost for LLMO: new content stays out of future models’ built-in knowledge, which is the only source for answers that run without retrieval. Each site weighs that against its reasons for keeping content out of training.

How does LLMO differ from GEO and AEO?

LLMO, GEO and AEO describe the same practice in most usage, with no agreed definition separating them as of early 2026. The LLMO name points at the model, including its training data; GEO and AEO point at the search engine or answer surface.

English Wikipedia’s article on generative engine optimization (GEO) lists large language model optimization among its other names and states that “No consensus definition distinguishing these terms had been established in the academic literature as of early 2026, and the terms are frequently used interchangeably.” A team doing LLMO and a team doing GEO will usually check the same crawlers, write the same kind of passages and track the same answers.

The names still carry different emphasis. GEO names the generative engine — the search product that retrieves and writes. AEO, the answer-engine variant, names the answer surface and grew out of featured snippets. LLMO names the model itself, which makes it the only one of the three whose name covers the training channel as well as retrieval. Where a distinction matters, LLMO is the term for work aimed at what a model says about a brand without searching.

Techniques only matter once their effect can be seen in model answers.

How do you measure LLM optimization?

LLM optimization is measured by running a fixed set of prompts across models on a schedule and counting brand mentions, citations and the accuracy of each description. Google Search Console's Generative AI report adds AI Overviews and AI Mode impressions.

• Mention rate — the share of answers to the prompt set that name the brand.
• Citation rate — the share of answers that link to the brand’s site as a source.
• Description accuracy — the share of answers whose statements about the brand are correct: what it sells, where it operates, who it serves.

Run each prompt more than once, in a logged-out session with the country and language fixed. A July 2026 survey of 45 GEO studies (arXiv 2607.14035) reports substantial run-to-run variability in AI answers, so a single answer is an observation, not a measurement.

The third metric is specific to LLMO. A model answering from training data can describe a brand confidently and wrongly, and only a reviewed sample of answers shows it. Search Console’s Generative AI performance report, available to all sites from 31 August 2026, covers Google’s AI features with impressions by page, country, device and date, but not mentions or accuracy. The full method for tracking brand visibility in AI answers sets out prompt selection, run frequency and the remaining metrics.

Grow AI Search’s own measurement is under way: Experiment 01, running, checks 50 fixed prompts weekly on 6 engines for 90 days from a brand-new domain. No results have been published yet.

These measures apply whatever the work is called, and the same prompt set can track the SEO side of the picture too. How the AI-answer practices sit alongside classic search work is set out in GEO versus SEO.

What does LLM optimization mean in machine learning?

In machine learning, LLM optimization means making a language model faster, smaller or more accurate. Techniques include quantization, pruning, knowledge distillation, mixed-precision training and fine-tuning. This sense concerns model engineering, not brand visibility in AI answers.

Iguazio’s glossary defines it as “the process of refining and enhancing the performance and efficiency of large language models.” The common techniques are:

• Quantization — storing model weights at lower numerical precision to cut memory use and speed up inference.
• Pruning — removing less important connections to make a model smaller and faster.
• Knowledge distillation — training a smaller model to reproduce the behaviour of a larger one.
• Mixed-precision training — combining 16-bit and 32-bit arithmetic to balance speed and accuracy during training.
• Fine-tuning — further training a pre-trained model on a specific dataset or task.

None of these affects how a model describes a brand. A search for “LLM optimization” returns both senses, so check which one a tool, article or job listing means before reading further.