What Is LLMO? How It Differs from SEO and Why Some Say It Is Pointless

LLMO (large language model optimization) is the work of helping AI answers from services such as ChatGPT and Gemini find your company and products and describe them accurately.

“I asked AI to recommend a service, and it only listed competitors.” Looking into LLMO, you may also find claims that it is pointless. This article covers what LLMO means, how it differs from SEO, why some call it pointless and what you can verify, how to measure results, and what to do in the first month.

What LLMO is: improving how AI answers present your company

LLMO is the work of improving how your company appears and is described in AI-generated answers. An LLM is an AI system that learns from large amounts of text and produces answers.

Semrush, an SEO tool provider, defines LLMO as a marketing tactic that aims to improve a brand’s visibility and portrayal in LLM-generated responses [1].

For example, a time-tracking tool provider (a hypothetical example) would check whether a question such as “Which time-tracking tool suits a company with 50 employees?” lists it as a candidate and describes its pricing and features correctly.

LLMO is sometimes called GEO or AEO. The differences between these labels are explained in LLMO, GEO, AEO and AIO: Understanding AI Optimization Terms.

LLMO vs. SEO: different screens and units of measurement

SEO looks at search results and rankings; LLMO looks at AI answers and cited sources for each question. The screen you check and the unit you count differ.

Aspect	SEO	LLMO
Screen checked	Search results page	AI answers
Unit counted	Keyword and URL rankings	Name mentions and cited sources per question
Main records	Impressions, rankings, clicks	Name mentions, links, accuracy of descriptions
Variability	Rankings change with time and conditions	Answers change even for the same question

The two cannot be separated, however. Google says there are no additional requirements for appearing in AI Overviews or AI Mode and that standard SEO best practices apply [2]. A page must be indexed and eligible to show a snippet [2].

This guidance covers Google’s AI features. Other services such as ChatGPT may not work the same way, so check actual answers.

Why some say LLMO is pointless, and what you can verify

LLMO gets called pointless because unproven tactics and verifiable work are often discussed together. Separating them makes it easier to decide what to do.

Three main reasons are given:

• Google says its AI features need no special optimization beyond SEO basics [2].
• New AI files or special schema.org markup are described as unnecessary [2].
• AI answers change even for the same question, making results hard to measure.

For structured data, Google also says it describes page content and should not describe information invisible to readers [3]. Adding machine-readable markup alone does not mean AI answers will select you.

The following work, by contrast, produces results you can check:

Work	What you can verify
Recording answers to customer questions	Whether your name and links appear and whether descriptions are accurate
Fixing gaps on public pages	Whether answers and conditions are now written on the page
Checking crawler settings	Whether search crawlers are blocked

OpenAI says OAI-SearchBot, used for ChatGPT search, and GPTBot, used for training, are configured independently [7]. Whether you blocked search access while intending only to refuse training is something you can verify. See How AI Crawlers Differ for details.

In short, the Google documentation we checked does not require AI-specific files or markup for its AI features. Whatever is proposed, ask for the evidence. Recording answers and fixing pages are tasks you can pursue while checking results.

How E-E-A-T relates to LLMO

Strengthening E-E-A-T helps readers verify information, but it alone does not guarantee AI citations. E-E-A-T stands for experience, expertise, authoritativeness and trustworthiness.

Google says E-E-A-T itself is not a specific ranking factor and that trust is the most important element [4]. It recommends making clear who created content, how and why [4].

That guidance concerns Google Search and does not describe effects on AI citations. Show sources and authors so readers can verify information, and check actual answers to see whether citations change.

Measuring LLMO: AI answers, Bing Citation Share and visits

Measure results by recording answers per question, Bing’s Citation Share and AI-referred visits separately. Combining them into one number hides what changed.

First, answers per question. Ask the same AI each chosen question several times and record separately whether your name appears and whether a link to your pages appears. Keep counts such as “a link appeared in 2 of 5 tests.” Also record the interface or model, whether search was on, and language and region, and start a new conversation each time. Results a tool collects through an API may not match what users see on screen, so note which one you recorded.

Second, Bing’s Citation Share. Launched in preview on June 16, 2026, it shows your site’s share of all citations across all sites for a specific grounding query, the search query the AI uses to find supporting sources [5]. It is separate from the citation rate you count from your own tests. Microsoft describes it as an observational metric, not a ranking or traffic share [5].

Third, site visits. Search Console’s Generative AI report shows impressions in Google’s AI features [6]. Clicks are not among the announced items, so check visits in your analytics. Some visits do not record their referrer, so treat the totals as what could be confirmed.

Record	What it shows	What it does not show
Answers per question	Name mentions and links	How many people saw them
Citation Share	Citation share in Bing	Visits
Generative AI report	Impressions in Google’s AI features	Clicks
Analytics	Visits to your site	What the answer said

The first month of LLMO

In the first month, move from a question list to recorded answers, settings checks and page fixes. You can start manually without extra tools.

The table below is an example plan we suggest. The number of steps is not mandatory, and it is not a deadline for results.

Week	Tasks	Output
Week 1	Choose 10 customer questions, separating those with and without your company name	Question list
Week 2	Ask each question 3 times in 3 AI services and record name mentions, links, sources and errors	Answer record
Week 3	Ask your web team to check robots.txt for blocked search crawlers	Settings check results
Week 4	Fix 1–2 pages missing answers or conditions and set the next check date	Updated pages and schedule

From month two, keep recording with the same questions and AI services. Record the date if you change questions or services. Google says recrawling can take several days to several months [2], so do not judge from one result right after a change.

To keep recording answers, see How to Use AI Optimization for AgentSignal’s AI citation rate workflow. Records help you see changes; they do not guarantee inclusion.