Large Language Model Optimization (LLMO): Meaning and Limits

Large Language Model Optimization (LLMO) is a practitioner label for efforts to influence how content, brands, or entities are described, mentioned, or cited in answers from LLM-powered tools. It is not a standardized discipline, and its boundaries with GEO and AEO are disputed.

SEO and Search

Large Language Model Optimization (LLMO): Meaning and Limits - Arfadia

Large Language Model Optimization (LLMO) is a practitioner label for work intended to influence how content, brands, or entities are described, mentioned, or cited in answers produced by tools built on large language models. The term is used in marketing publications, but writers disagree about where it ends and generative engine optimization (GEO) or answer engine optimization (AEO) begins. The Google Search and OpenAI documentation reviewed here does not describe LLMO as a ranking protocol.

Understanding the term starts with three questions: by which pathways information can reach an AI-generated answer, what a website owner can and cannot influence, and what a mention or a citation does and does not prove.

What LLMO Means and Why Definitions Differ

In its marketing sense, LLMO stands for Large Language Model Optimization. Publishers that use it generally describe efforts to make a brand or its content more likely to appear, be represented accurately, or be cited when someone asks an LLM-powered assistant a question. Agreement mostly ends there.

How Marketing Publishers Use the Term

A dated example anchors the term. Olaf Kopp's article for Search Engine Land, published on October 12, 2023, used the LLMO abbreviation in a discussion of generative AI outputs. That record shows the label was in professional use by that date. It does not show who coined it, when it first appeared, or how widely it spread.

Later guides frame it in different ways. Search Engine Land's guide to large language model optimization, updated in November 2025, defines the practice and compares it with SEO, AEO, and GEO. Other practitioner writing, including the same publication's comparison of SEO and GEO, describes these labels as overlapping. Some writers treat LLMO and GEO as near-synonyms, while others present LLMO as a narrower emphasis on language models and conversational tools. These are positions taken by named publishers. They are not a measured consensus, and none of them is a technical specification.

A Different Meaning in Engineering

Outside marketing, the same words point somewhere else. In machine learning engineering, LLM optimization usually means making a model run faster or use less memory, the subject of technical material such as Hugging Face's documentation on optimizing LLMs for speed and memory. A 2025 research paper even uses LLMO as shorthand for a Large Language Model Optimizer, an idea unrelated to marketing visibility. When a result about LLMO discusses inference speed, memory use, or training settings, it is probably using the engineering sense.

LLMO, GEO, AEO, and SEO: Where the Labels Overlap

Most of the confusion around LLMO comes from its neighbors. GEO, AEO, and SEO all deal with visibility in search or answer experiences, and the vocabulary has grown faster than any agreed taxonomy. No settled hierarchy places LLMO above GEO, or GEO above AEO. When a vendor presents such a hierarchy, it is describing its own framework, not a provider rule.

What Google Says About These Labels

Google's AI optimization guide for Search recognizes that practitioners use terms such as AEO and GEO, and it describes optimizing for its generative AI features in Search as a continuation of SEO. That position is useful, but its scope is Google Search. It does not settle what LLMO means for ChatGPT, Claude, the Gemini app, or any other assistant.

How Each Label Is Commonly Framed

The table summarizes how named publishers and Google frame these labels, and what the available evidence leaves open. It is not an industry taxonomy. Definitions are disputed, and the labels overlap.

Label	How it is commonly framed	What the evidence does not settle
LLMO	Influencing how LLM-powered answers describe, mention, or cite a brand or source; some publishers treat it as a near-synonym of GEO, others as a narrower emphasis on language models	Whether it is a distinct discipline; the Google Search and OpenAI documentation reviewed here describes no LLMO ranking protocol
GEO	Visibility of sources in responses from generative engines, the setting studied in the 2024 KDD research on generative engine optimization	Whether it is broader than, narrower than, or the same as LLMO
AEO	Visibility as a direct answer; Google recognizes AEO as a practitioner term alongside GEO	A universal line between answer-oriented work and AI-generated conversation
SEO	Organic search discovery and visibility; Google describes work on its generative AI features in Search as a continuation of SEO	How assistants outside Google Search select and cite sources
AI citation	A visible source reference in an AI-generated answer, distinct from a brand mention	Whether a citation reflects endorsement or leads to visits or sales

Because the labels overlap, the same piece of work can be described with more than one of them. A label on its own says little about what was actually done or measured.

How Information Reaches an LLM-Powered Answer

The most useful idea behind LLMO is that an AI-generated answer can draw on information through different pathways. Each pathway behaves differently, and a website owner relates to each one differently. Mixing them up produces many of the inflated promises attached to the term.

Knowledge Stored in Model Parameters

A language model learns statistical patterns during training, and that learning is stored in its parameters, often called weights. Lewis and colleagues described this as parametric memory in their 2020 paper on retrieval-augmented generation. After a hosted proprietary model has been trained, publishing or editing a page on your website does not directly rewrite those parameters. Organizations that control a model, or that work with open-weight or customizable models, can change parameters through an actual training or fine-tuning process. That is model development, not website optimization.

From the outside, a publisher usually cannot confirm whether a page was selected for a training dataset or what a model retained from it. No universal timeline connects publishing a page to its influence on a later model.

Retrieval and Grounding

The same 2020 paper paired parametric memory with non-parametric memory, a separate index that the system searches while it generates an answer. In that study the index was built from Wikipedia. Commercial products apply their own versions of the retrieval idea. OpenAI's API offers a web search tool that lets a model look up current information and return sourced links. Google describes AI features in Search that can run additional related searches, a technique it calls query fan-out, to find supporting pages.

These are examples, not a census. The evidence reviewed here does not show how often commercial assistants search the web for a typical question, so no one can honestly promise that every AI answer comes from a fresh live search, or that none does.

User-Triggered Fetching

A third pathway is narrower. Some assistants fetch a specific page because a user asked them to, for instance by sharing a link or requesting a lookup. OpenAI documents a ChatGPT-User agent for certain user-initiated actions, and Anthropic describes a Claude-User agent for user-directed requests. A visit of this kind shows that a page was opened. It does not mean the page was used for training, and it does not mean the page will be cited in other people's answers.

Figure 1. Three pathways by which information can reach an LLM-powered answer.

What Publishers Can and Cannot Control

Once the pathways are separated, the practical question gets simpler. Which levers sit with the publisher, and which sit with the provider?

Access Is Not Selection, and Selection Is Not Citation

A publisher can decide whether specific crawlers may request its pages. That decision matters, but it is only the first stage of a longer chain. Allowing a crawler does not prove that content was selected for a dataset, ingested, retrieved later, cited, or connected to a sale. Each of those is a separate event, and most of them happen inside systems the publisher cannot inspect. The chain has five stages:

Crawler access: a bot is permitted to request a page.
Selection or retrieval: content enters a dataset or an index, or is found during a search at answer time.
Answer generation: the model writes a response that may or may not use the content.
Visible citation: the interface shows the page as a source.
Visit or conversion: a reader clicks through and takes an action.

Evidence of one stage is weak evidence of the next. Server logs can show the first stage. Some provider reports show parts of the fourth. Site analytics can show the fifth. Very little that a publisher can observe from outside reveals the second.

Crawler Roles Are Provider-Specific

Providers document separate user agents for separate jobs, and the controls for them can be set independently. The table summarizes roles described in OpenAI's overview of its crawlers and in Anthropic's help article on web crawling.

Provider	User agent	Documented purpose
OpenAI	OAI-SearchBot	Access for search features
OpenAI	GPTBot	Crawling that may be used to train models
OpenAI	ChatGPT-User	Certain actions initiated by a user
Anthropic	ClaudeBot	Collecting web content that may contribute to model training
Anthropic	Claude-SearchBot	Crawling to improve the quality of search results
Anthropic	Claude-User	Fetching pages when a user's request calls for it

Two cautions apply. These descriptions come from each provider about its own systems and say nothing about other assistants. A documented purpose is also not a promise of outcome: OpenAI does not guarantee that allowing its search crawler will place a page in responses.

Google Search Eligibility and Controls

Google publishes its own conditions for AI features in Search. Its guidance on AI features says a page needs to be indexed and eligible to show a snippet to appear as a supporting link, with no additional technical requirements. A later Search Console setting, documented in Google's help center, adds a generative AI inclusion control with include, exclude, and inherit options. Those options are specific to Google, and their current state has to be checked property by property. Google also states that meeting its requirements never guarantees crawling, indexing, serving, or citation in any particular answer.

The fundamentals Google points to are ordinary search practices, the territory of SEO services, and they apply to Google's AI features in Search. They do not automatically describe how any other assistant selects or cites sources.

Where llms.txt Fits

llms.txt is a proposal, first published by Jeremy Howard on llmstxt.org in September 2024, for a Markdown file that gives AI agents a curated guide to a website. It is not an access directive like robots.txt, and no provider requirement makes it universal. Google's Search guidance says special files such as llms.txt, tiny content chunks, and AI-specific markup are not needed for its AI features in Search and bring no special ranking or visibility advantage there. That statement covers Google Search only.

Usage evidence is limited. Ahrefs reported that among 137,210 traffic-receiving domains using Ahrefs Web Analytics in May 2026, about 38,000 had an llms.txt file, and 97% of those files recorded zero requests. (Metric: recorded requests to the file. Population: Ahrefs Web Analytics customer domains, not a census of the web. Geography: not stated in the sample definition. Confidence: REPORTED.) Because the sample consists of Ahrefs customers, it skews toward technical, SEO-aware sites, and the study did not test whether the files conformed to the llms.txt specification. A request count shows that something fetched a file. It does not show that an AI system read, trained on, retrieved, or cited the content, and it says nothing about business benefit.

Figure 2. Five separate stages between crawler access and a business outcome.

What LLMO Outcomes Actually Mean

Reports about LLMO results often blend several signals. Keeping them apart prevents conclusions the data cannot support.

Mentions, Citations, and Recommendations

A brand mention is the name appearing in an answer. A citation is a visible source reference, usually a link. A recommendation is an answer that favors one option over others. These can occur independently: an answer can mention a brand without citing its website, or cite a page while recommending something else. Referral visits, conversions, and impressions recorded by a provider are further, separate endpoints. A citation shows attribution. It does not show endorsement, and it does not prove that any change caused an improvement.

Some providers now report part of this picture. Microsoft's AI Performance dashboard in Bing Webmaster Tools, introduced in public preview in February 2026, separates total citations, cited pages, and sampled grounding queries. Those are metrics for Microsoft's own AI experiences. They are not a readout of model memory or a universal probability of being cited.

Why Prompt Observations Are Limited

Running a fixed set of prompts and recording which sources appear is a common way to look at AI answers. It produces observations, not experiments. The observer chooses the prompts, the timing, and the product, so the results describe that sample rather than every question real users ask.

Controlled research shows how much study design matters. Aggarwal and colleagues published a study of generative engine optimization at KDD 2024 that used a defined benchmark, retrieved sources, model-generated responses, and visibility measures specific to the study. It is careful work, but it tested particular interventions in a particular setup. Its findings cannot be read as general proof that LLMO, in the marketing sense, works or fails.

Limitations and Responsible Interpretation

A few limits apply to any discussion of LLMO, including this one.

Training data is largely opaque. Outside parties generally cannot trace which pages shaped a proprietary model, or how strongly.
Provider documentation is product-specific and changes over time. Guidance written for Google Search does not transfer automatically to other assistants, and dated documents need rechecking.
No endorsed LLMO ranking protocol appears in the Google Search and OpenAI documentation reviewed here. That is a finding about those documents, not proof that no provider has one.

Responsible work in this area starts with what can be checked: which crawlers a site actually allows, whether content is accurate and clearly attributed, and how provider reports and site analytics change over time. For help with visibility in generative search, see Arfadia's generative engine optimization services, with the understanding that no service can control how a third-party model is trained or guarantee citations in its answers.

Frequently Asked Questions

What is LLMO?

LLMO stands for Large Language Model Optimization. It is a practitioner label for efforts to influence how content, brands, or entities are described, mentioned, or cited in answers from LLM-powered tools. It is not a standardized discipline, and publishers define its boundaries differently.

Is LLMO the same as GEO?

Some publishers use the two terms almost interchangeably, while others present LLMO as a narrower emphasis on language models and conversational tools. There is no settled hierarchy between them. Google treats work on its own AI features in Search as a continuation of SEO, which does not settle the question for other assistants.

How is LLMO different from AEO?

AEO focuses on making content usable as a direct answer, and that can include AI-generated answers, not only featured snippets or voice results. LLMO is usually framed around language-model tools specifically. The two overlap, and no universal rule separates them.

Can editing my website change what an LLM already knows?

Not directly. A hosted model that has already been trained stores what it learned in its parameters, and publishing or editing a page does not rewrite them. A page can still matter if a system retrieves it at answer time or collects it for future training, but neither outcome is guaranteed or visible from outside.

What is the difference between GPTBot and OAI-SearchBot?

OpenAI documents them as separate crawlers with separate purposes. OAI-SearchBot is used for search access, while GPTBot covers crawling that may be used for training. ChatGPT-User is a third agent for certain user-initiated actions. Controls for them can be set independently, and none of them guarantees that a page will appear in responses.

Is llms.txt required for LLMO?

No. llms.txt is an optional proposal for a Markdown file that gives AI agents a curated guide to a site. Google says it is not needed for its AI features in Search. Publishing the file, or seeing requests for it in server logs, does not show that any AI system used or cited the content.

What does an AI citation indicate?

An AI citation means an answer displayed a source reference, usually a link, to a page. It shows attribution in that answer. It does not show that the answer endorsed the source, that the brand was recommended, or that the citation led to visits or sales.

Related Terms

Generative Engine Optimization (GEO): Practitioner term for the visibility of sources in responses from generative search and answer engines.
Answer Engine Optimization (AEO): Practitioner term for visibility as a direct answer, a use that can extend to AI-generated answers.
AI Citation: A visible source reference in an AI-generated answer, distinct from a brand mention.
Google AI Overviews: Google's AI summaries in Search and the conditions for supporting links, a Google-specific surface.