LLMO vs SEO: Understand the difference and boost your brand visibility

In content strategy, the debate around LLMO vs SEO is shaping how brands approach digital visibility. Search Engine Optimisation (SEO) has long been the standard for helping web pages rank on search engines like Google. However, with the rise of artificial intelligence (AI) and tools powered by large language models (LLMs), a new strategy called Large Language Model Optimisation (LLMO) has emerged.

While SEO targets rankings through keywords, meta tags, and backlinks, LLMO ensures content is relevant and accessible for AI to surface in its responses. Understanding the differences and how both can work together is crucial for businesses looking to thrive in search engines and AI-generated environments. Read on to explore LLMO vs SEO and how to optimise for both to boost your digital presence.

How Google prefers optimising content

Google evaluates key aspects of your site, such as page structure, content relevance to user intent, and clarity of language, to determine its placement in organic search results. Using strong semantic SEO—focused on concise language, logical sections, and actionable answers—can significantly improve your ranking.

Below are the key points on how Google wants content made, so your pages show up on the first page of search engines.

Google’s ranking priorities

Google uses rules to pick which pages show up first in its search list. These rules focus on what people are looking for, how useful the page is, and if it comes from a trusted source. This means that when making or checking lots of pages, teams should check if the content matches what the user wants, if it answers needs, and if it adds value.

The Search Quality Evaluator Guidelines help explain what Google looks for when it checks content. They also help brands do better at ranking, plan smarter content creation, deal with search volatility, and make better competitor analysis choices, especially when aiming for top spots on SERPs using strong search engine optimisation basics.

Helpful Content System and E-E-A-T

Google’s Helpful Content System is designed to lower the visibility of shallow or low-value pages in search results. It prioritises content that is written for people and not just for ranking. A major part of this system includes E-E-A-T, which stands for:

• Experience: Does the content show the author has direct, first-hand involvement with the topic? For instance, a review written by someone who has used a product will likely rank higher than one written without personal insight.
• Expertise: Is the author skilled or knowledgeable in the subject area? Content written by someone with a relevant background, training, or deep understanding of the topic will likely be trusted and ranked well.
• Authoritativeness: Does the site or author have a strong reputation in their field? Citing industry sources, earning mentions from other credible websites, and publishing on a site known for that subject all help improve authority.
• Trustworthiness: Can users rely on the content's accuracy, safety, and honesty? Including transparent sources, clear authorship, facts, and a secure website all contribute to trust.

To meet these standards, brands can build a structured content workflow. This might include assigning writing tasks to subject-matter experts, reviewing each piece for factual accuracy, adding trusted references, and applying internal quality control before publishing. These steps boost credibility and support a smart content marketing approach that improves visibility and builds trust with users and search engines.

Hallucinated sources

Some AI tools may give wrong or made-up answers when they try to fill in missing facts. These mistakes, called hallucinations, are different in every AI tool, so you must fix them to fit the tool you’re using. The best way to stop these errors is to use source-checking steps and tools like retrieval-augmented generation (RAG). This helps avoid text that might fabricate data or include fake information. It also helps you clean up factual errors and manage AI references more safely and reliably.

What are Large Language Models?

A large language model (LLM) is an AI system that helps computers read and write like people. It learns by studying huge amounts of training data, such as books, websites, and articles. This is done through deep learning, where the model finds patterns in words to predict what word should come next—a process known as token prediction.

How does it work?

Large language models are built using the transformer architecture. This type of computer design helps the model understand the meaning of words in a sentence by looking at how they connect to each other.

Why do businesses use it?

Businesses use AI tools like ChatGPT to save time and work faster in writing emails, answering customer questions, and finding useful info quickly—called knowledge retrieval. With the right prompting, brands can better guide these tools. Some also try LLM optimisation to make sure their content shows up clearly and supports their goals.

What is LLM Optimisation?

LLM optimisation (LLMO) is the strategy used to ensure that writing content is processed correctly by AI tools such as Claude and Google’s AI overviews. In simple terms, LLMO is about making your content ‘AI-friendly’. A strong content strategy and well-structured content make a big difference in how LLMs read and share your pages.

What does LLM Optimisation focus on?

To appear in AI-generated answers, your content must be clear, relevant, and easy for LLMs to understand. These are the key areas you should focus on:

Embedding relevance

Your content should directly match what users are searching for. LLMs select pages that provide complete, direct answers to specific questions. Stay focused on the topic and avoid adding unrelated information.

Semantic clarity

Use simple, clear words that are easy for both people and AI to understand. This helps AI identify the main idea and connect it to related queries. Avoid jargon unless necessary.

Source visibility

LLMs are more likely to use your content if it’s easy to read and verify. Use proper heading tags (H1, H2, H3), keep your layout clean, and cite credible sources. Add sources or facts where needed, and use schema markup if possible, so the AI knows where your info came from.

What is the difference between SEO and LLMO?

Search engines and AI tools look at content in different ways. Traditional SEO helps your page get seen in Google by helping with indexing and driving organic traffic to your site. On the other hand, LLMO ensures your content is structured and clear so it can be effectively understood and potentially referenced in AI-generated responses.

To reach more people, it's smart to learn how both work. Below, we show the key ways SEO and LLMO differ and how they can help you reach your content goals.

Target audience

SEO is made for users who type keywords into a search bar. These users look for helpful links in the organic search results. This makes SEO good for reaching users with a clear need or goal. On the other hand, LLMO focuses on making content accessible for AI tools, which may present it directly in chat responses without linking to a source. So, LLMO is more about helping your brand show up in zero-click answers. This makes each method better for a different target market or audience.

Optimisation focus

SEO helps pages rank by optimising elements like title tags, building quality links, and structuring content for readability and crawlability. It aims to align with Google's search algorithm to improve indexing and attract organic traffic.

In contrast, LLMO focuses on AI content optimisation. It helps content appear in AI tools by ensuring the words match what users ask. This includes clear writing, comprehensive topic match, and useful answers.

LLMO also supports personalisation. A key aspect of LLMO is recognising that LLMs excel at processing natural language. Therefore, writing in a conversational tone makes your content more engaging and relatable.

Content consumption

With SEO, people click on results and visit your site, so you can track views and measure web traffic. However, with LLMO, your content may appear as a snippet in a generative AI search, meaning users might not visit your site. That’s why a clear and logical content structure is important, as AI tools prioritise content from which they can read and extract info quickly.

This structure often contributes to overall smooth navigation for users who choose to visit your site. This also changes how you see engagement, since it’s not just about visits but how your content adds to the user experience even when it’s not viewed directly on your site.

End goal

In SEO, the goal is to drive visits to your site to increase sales, leads, or ad views, which supports traffic growth. In LLMO, the aim is different; you want your content to appear in AI replies, even without clicks. This improves brand visibility and builds trust through citations. Instead of focusing solely on visits, the value comes from showing up in AI answers. This can lead to long-term efficiency and better ROI (Return on Investment), meaning you get more value from the time and resources spent on your content.

How to get referenced by ChatGPT?

Getting referenced by an LLM like ChatGPT through LLMO, referred to as LLM SEO, is a different game than traditional search engine optimisation. Unlike a traditional search engine that directs users to your website via a clickable link, ChatGPT primarily synthesises and presents information directly to the user. The goal isn't just a click; it's to be considered a trusted, relevant source by the AI.

Take a look at the key ways below to help your page get picked up and shown by ChatGPT.

LLM indexing vs crawling

Unlike traditional tools that scan the web through web crawler systems and store pages using sitemap files, LLMs don’t work the same way. They don't crawl your site live or check updates in real time. Instead, they are trained using large sets of text data called ‘training datasets’. They can also use embeddings or external sources when responding to queries through real-time tools like Retrieval-Augmented Generation (RAG).

This difference changes how enterprise websites must think about content reach. For LLMs, it’s more about how clear, well-structured, and helpful your content is to a web query, rather than how it fits into the web hosting service setup or traditional SEO crawling pipelines. Your content might not be directly pulled in from the web like Google would, but it can still influence AI outputs through trusted inclusion or summarisation from available datasets.

Role of high-authority sources

Just as search engine indexing relies on structured data to understand your page, LLMs benefit immensely from well-organised content. This means content from high-authority websites is more likely to show up in LLM answers since these tools aim to pull reliable, well-known information. It doesn’t mean you can force ChatGPT to quote you, but it helps if your brand has a strong reputation, builds trust, and is connected to a clear and relevant domain name.

Working with respected media outlets, building backlinks from top sites, or earning citations in popular blogs can improve your chances. When LLMs are trained or retrieve info, they tend to rely more on sources already linked to trust signals, even though there’s no guarantee your page will appear in any given result.

Why some links appear (and some don’t)

Not every AI answer includes a source link. When they do, it’s often because that content directly answered the question clearly or was part of a verified retrieval system. Even then, some content gets left out. This can happen because of missing or vague metadata, limited relevance based on the search algorithm, or strict content filtering built into AI systems to avoid low-quality or risky results. So even if your page is helpful, it may not get linked or shown in an answer.

Should LLMO be part of your strategy?

Yes, LLMO should now be part of your digital marketing strategy, especially if your brand wants to stay visible and trusted as users shift from traditional search to AI tools for answers.

More users now ask ChatGPT, Claude, or Google’s SGE for instant information. These AI tools don’t just list links—they generate direct responses by scanning and learning from existing online content. That means ranking high on Google is no longer enough. Your content needs to be written and structured in ways that AI can read, understand, and reuse effectively.

This is where LLMO becomes essential. It doesn’t replace SEO but strengthens your strategy by making your writing more complete, precise, and AI-friendly. It helps language models better align your material with user prompts, increasing the chance of appearing directly in generated responses.

Adding LLMO to your strategy is not about chasing clicks or traffic metrics. Many AI tools don’t drive users to websites at all. Instead, the focus shifts to brand authority; appearing in trusted responses builds recognition, positions your brand as a credible source, and reinforces your expertise without needing a visit.

Aligning LLMO with existing SEO workflows

LLMO is still a new approach. There are no fixed rules yet, but you can already start using LLMO ideas in your current SEO workflow. This means refining how you create and publish content, rather than starting from scratch.

Traditional SEO still matters; keyword use, internal links, and crawlability remain essential. However, LLMO adds a new layer by focusing on:

• Clear and straightforward writing
• Good structure (titles, headers, short blocks)
• Showing real experience and trust
• Adding schema markup so AI tools can better understand the page.

Schema markup helps search engines and AI tools know what your page is about. It can tag items like product reviews, how-to steps, or FAQs. This makes your page easier to pick up in AI results.

LLMO also depends on a strong content strategy. That means writing full, helpful answers that match what users are asking and not just aiming for clicks. This makes your content more useful to both people and AI models.

Combine LLMO and SEO with QWERTYLABS for smarter digital reach!

SEO and LLMO serve different roles but work best together. SEO helps your site rank in search engines, while LLMO makes your content easier for AI tools like ChatGPT to read and reuse. The smart approach is to combine both—optimising for search while writing with clarity, structure, and accuracy so your pages appear in AI-generated responses. These insights help users find and trust your content.

Whether you run an online casino, sportsbook, or affiliate website, using both strategies helps you reach more users across Google and AI platforms. At QWERTYLABS, we provide SEO and LLMO expert solutions tailored to boost your rankings, grow your visibility, and position your content for success in both search and AI results. Contact us today and take the next step toward smarter digital growth!

Frequently Asked Questions (FAQs)

How does LLMO relate to E-E-A-T principles?

LLMO follows the same core ideas as E-E-A-T: show real experience, clear expertise, accurate facts, and honest intent. These help AI systems decide if your content is useful and trusted.

What is LLMO and how does it differ from SEO?

LLMO means ‘Large Language Model Optimisation.’ SEO focuses on rankings in search engines. LLMO makes your content readable and useful for AI tools like ChatGPT. It focuses on clarity, structure, and helpful detail, not just keywords or links.

Are there tools available for optimising content for LLMs?

The primary ‘tool’ for LLMO is your content strategy and how you write. However, you can use tools like Grammarly (for clarity), Hemingway Editor (for readability), or more advanced AI writing assistants (like Jasper, Copy.ai), which can help you write clearly and concisely, which benefits LLMO.

How does LLMO support brand positioning?

LLMO helps your brand appear in AI-generated responses with clear, helpful content. This builds trust and shows your value, even when users don’t click through to your site.

What types of content benefit most from LLMO?

Helpful pages like FAQs, how-to articles, tutorials, and expert guides work best. They give clear answers that AI tools can pull and share in user prompts.

Can branded content appear in AI answers?

Yes—if the content shows real value. Focus on facts, expertise, and usefulness. AI systems select content that answers questions clearly and with authority.

Is LLMO only valuable for organic traffic gains?

No. LLMO also builds brand trust, authority, and visibility inside AI responses. Even if users don’t click a link, your brand still earns reach and recognition in AI tools.

Dea Narido

With a strong passion and background in digital marketing and specialisation in SEO writing, I aim to provide high-quality contents that propels your online presence forward. By utilising keyword research and optimisation tenchniques, I ensure you that your website and online casino platforms will rank prominently in search engine results. Start attracting your target traffic and drive conversions with me today!