Large Language Model Optimization (LLMO) is all about getting LLMs to pick up your brand in their responses. According to Adobe Analytics, generative AI traffic has grown by 1,200% between July 2024 and February 2025, signalling that it may be time to go beyond traditional SEO and include LLMO in your strategy.
In this guide, I'll show you how to do so effectively in seven steps.
What you will learn
- What LLM SEO is and why it matters for digital marketers
- How search behavior is changing with the rise of generative AI
- How to structure and optimize content for AI-driven discovery
- Real-life examples of websites leveraging LLM SEO
What is large language model optimization?
Large language model optimization is the process of adjusting your content so that LLMs can find and analyze it more easily. The goal is to get different models (GPTs, Claude, etc.) familiar with your brand and content so that it can be included in the responses.
The importance of an effective LLMO strategy keeps growing with the rise of AI chat platforms like ChatGPT and Gemini.
People are increasingly turning to such platforms for answers they've previously searched for through traditional search engines, most notably Google.
In fact, Google's search market share dropped below 90% in October 2024, which hasn't happened since March 2015. While it still dominates the landscape, AI chatbots are undoubtedly starting to get their piece of the cake.
That's why it's only a matter of time before traditional SEO might not be enough to ensure content visibility and discoverability. You'll need to make sure LLMs are well-familiar with your brand and can reference it in their responses.
For example, Surfer appears frequently in LLMs for related queries. When we asked ChatGPT about the best content optimization tools, Surfer popped up right away!
Tools like Claude, Perplexity, and other AI-powered solutions now influence how people retrieve information, and this isn't likely to change. Instead of seeing it as a fleeting trend, you should adjust your SEO strategy to account for LLMs.
SEO vs LLM Optimization
SEO practices focus on ranking websites in search engine results while LLM optimization methods aim for your pages to appear as credible sources in LLM generated responses
Traditional search engine optimization (SEO) focuses on ranking pages in search engines, most notably Google. As such, it involves tweaks that adapt pages to specific search algorithms so that they can be indexed and served for the relevant keywords.
LLM optimization has a broader scope.
Instead of focusing on one specific AI platform, it aims for content discoverability across conversational AI tools. With an elaborate strategy, your content can appear in various LLM-powered tools and search engine features like Google's AI Overviews (AIOs).
How LLMs affect search results
Large Language Models generate direct answers to satisfy user intent reducing the need to click through multiple links, potentially lowering traffic to traditional websites. Overall, LLMs shift search from a list of website pages to direct information delivery.
LLMs are nudging search from keyword-based queries to natural language questions. Tools like ChatGPT and Claude interpret intent and context through natural language processing, offering results that better match conversational input.
This reduces dependence on exact keyword matches and prioritizes content that answers questions clearly and directly.
The Adobe research I mentioned at the start of this guide highlights this shift. It showed a 1,300% increase in AI search referrals to U.S. retail sites during the 2024 holiday season, confirming the growing influence of AI on search patterns and consumer behavior as a whole.
Unlike traditional search algorithms that use one source to provide answers (e.g., Google's Knowledge Graph), LLMs summarize multiple sources.
This reduces the visibility and clicks to websites unless the content is directly cited or paraphrased.
Take Perplexity as an example.
It uses citations to show the source of its answers, so only the cited websites can enjoy visibility. By contrast, sites that may be ranking well in Google but aren't authoritative enough to be picked up by Perplexity end up losing AI traffic.
This is because users now seek concise, high-authority answers rather than scrolling through traditional search results.
For example, I asked ChatGPT to compare the benefits of investing in stocks and ETFs using a table so that I don't have to browse different websites.
It gave me a clear summary with links to its sources like so:
If I wanted to dig deeper by visiting the suggested links, I could choose between Investopedia, Fidelity, and Morningstar because ChatGPT found them the most authoritative.
Unless I wanted to expand my search through Google, first-page results like those in the image below would lose traffic despite being SEO-friendly because ChatGPT doesn't consider them authoritative enough.
Why you should include LLMO in your SEO strategy
You should include LLMO in your SEO strategy because it aligns content with how language models interpret queries. This way, it increases your chances of being cited in AI answers and enhances content clarity, authority, and snippet potential across search platforms.
LLM-friendly content aligns with the best practices for the more traditional features like Google's featured snippets.
Through LLM optimization, you can kill two birds with one stone to help your content appear in both AI platforms and Google's results.
These benefits aren't only theoretical — we have plenty of cold figures that support them.
At the beginning of 2025, Google's CEO Sundar Pichai revealed that AIOs have reached 1.5 billion monthly users, highlighting the crucial integration of AI in search experiences.
At the same time, the user bases of major AI platforms like Gemini and ChatGPT are expanding rapidly, with 350 million and 600 million monthly users as of March 2025, respectively.
Combine this with the drop in Google's search market share, and it's clear that you should focus on getting your content picked up by LLMs instead of only aiming for high search engine rankings.
Failure to do so can expose your business to many risks, most notably:
- Considerable traffic drops
- Loss of organic visibility
- Brand dilution (especially in terms of reputation and authority)
7 practical strategies for optimizing content for LLMs
Getting your content to appear in AI answers isn't as daunting as it may seem—here's how to do it:
1. Use entities
An entity is a person, object, place, or any other concept that search engines and LLMs can understand. Unlike keywords, entities rely on contextual relationships to help search algorithms understand the intent behind a search query, so entity optimization is far more important than traditional keyword optimization.
In the context of your business, using entities would mean using your brand name and related terms across online channels so that they can be picked up and analyzed by LLMs.
You should aim for descriptive entity references like:
- Company name
- Locations and people associated with your company
- Product lines under your brand
If you have the necessary technical background and bandwidth, you can use Google’s Natural Language API for entities, specifically for tasks like entity recognition, extraction, and sentiment analysis.
Alternatively, you can opt for a user-friendly option like Surfer's Content Editor.
It spares you exhausting research by outlining the entities you should use in your content and their optimal usage.
Use as many entities as you can without forcing them in, and you can create the semantic relationships LLMs and search engines look for.
To strengthen entity visibility and make sure LLMs don't encounter confusion while detecting relationships, make sure your NAP (Name, Address, Phone) citations and brand information are consistent.
Consistency improves how accurately and confidently LLMs identify and associate your brand with relevant queries, enhancing your likelihood of being recommended.
You can use dedicated tools like BrightLocal's Citation Tracker to find all NAP citations across the web and update them as needed.
As a side note, there's a misconception that using structured data markup helps your content get picked up by LLMs.
It stems from the way traditional search engines work, which isn't the same way LLMs analyze and serve data.
Specifically, they use Retrieval Augmented Generation (RAG) for their search index, not structured data.
So while you should use schema markup for traditional SEO, doing so won't have any direct impact on your content's visibility in LLMs. Instead, focus on entity optimization instead of the technical SEO aspect.
To show you how entities work in the context of LLM optimization, I asked ChatGPT to recommend a few budget-friendly laptops.
As you can see in the image below, it suggested a few options from brands like Acer and Lenovo.
By contrast, options like MacBook didn't make the cut because they're neither marketed as budget-friendly nor generally considered cost-effective.
Instead, ChatGPT scoured the web for brands that associate themselves with budget-focused entities to make its suggestions.
If you look at the resources, you'll see they include:
- Articles
- YouTube videos
- Reddit posts
This speaks volumes about using entities in various channels, not just your web pages.
Build a multi-channel entity optimization strategy that connects your brand name to relevant entities around the web to maximize its chances of showing up in AI responses.
2. Build trust with Google’s Knowledge Graph
LLMs often use Google’s Knowledge Graph and other public knowledge bases like Wikipedia to verify entities. The more your brand appears in them, the higher the chances of getting a citation in generative answers.
The Knowledge Graph is Google's massive database that the search engine uses to find and serve information relevant to a query in the Knowledge Panel next to the search results, like so.
The above example shows a Netflix show that just came out (as of this writing), yet Google's Knowledge Graph already has plenty of credible information that can be served in the infobox.
This shows the value of a strong brand identity, which translates into AI recognition because many LLMs will tap into the Knowledge Graph to generate their responses.
There are plenty of ways to get your brand listed in the Knowledge Panel, most notably:
- Building consistent citations (like I explained in the previous step)
- Claiming your Google Business Profile if you have one
- Submitting your entity to Google via the Search Console's “Knowledge Panel” feedback form (you can find the steps here)
While LLMs don't care about structured data, the Knowledge Graph does. You can go to schema.org to discover the many types of properties to focus on, but the most important ones include:
- Organization
- Person
- SameAs
Using schema markup effectively strengthens brand recognition, which builds rapport with the Knowledge Graph.
So while it doesn't directly affect AI responses, it supports your brand because there's a higher chance of an LLM pulling it from the Graph.
If you want to see the connection between the Knowledge Graph and LLM responses in action, take Cleveland Clinic as an example.
It has a verified knowledge panel with consistent schema markup, which makes it a common reference in medical-related queries across LLMs.
3. Answer questions users are asking on LLMs
Among the many changes it brings, LLM optimization prioritizes question-based queries like those people type into AI chatbots.
For example, instead of optimizing for "blockchain definition," you should use "what is blockchain" because that's what a user is more likely to search for.
Your content creation strategy should focus on content that targets these question-based natural-language queries.
There's no room for clunky keywords or strangely-phrased queries in LLM optimization, so put yourself in your audience's shoes and consider the simplest and most natural way they'd search for something.
If you need help, you can turn to the same AI platforms you're optimizing for. For example, ask ChatGPT to generate real question queries that users might input into LLMs.
Alternatively, you can use the People also ask section in search results to get more inspiration.
You can also use third-party tools like AlsoAsked and AnswerThePublic to find plenty of question-based queries. Enter the seed keyword, and you'll get various suggestions to pull from.
To assess the real-life results of a content strategy revolving around LLM-friendly queries, Steve Toth analyzed the Connecteam website, which seemed to dominate AI responses, especially in ChatGPT's Deep Research.
According to Steve's research, the reason for this outstanding performance is highly targeted content that answers specific queries and caters to niche audiences.
Take a page from Connecteam's book, and write content that directly answers users' questions.
If you want to see how this is done in practice, the Avast blog is a great example. It has dozens of Q&A-style articles ranking and getting picked by AI tools.
4. Develop user-generated content
LLMs often tap into reviews, forums, and community answers when providing responses. The more you encourage users to write content surrounding your brand, the more trust and visibility you'll build.
You could see an example of this in my budget laptop question, when ChatGPT used Reddit as one of its sources.
In fact, Reddit is one of the main sources of user-generated content—Google even made an arrangement with it to use Reddit answers as training data for its LLMs.
Another good example is Grok, which uses X posts as training data. While this can lead to issues like bias, it shows the importance of ensuring users interact with your brand and talk about it online.
If you're not sure where to start with user-generated content, Reddit would be your best bet. Depending on your industry, you can also leverage Stack Overflow, Discord, and branded forums (bonus points if you run your own branded forum).
User-generated content is particularly useful for commercial queries, especially when it comes to widely used products.
For example, when I asked ChatGPT to recommend the best fluoride-free toothpaste, it used Reddit pretty heavily to suggest a few options.
5. Stick with SEO best practices
Contrary to what you may have heard online, AI isn't replacing SEO.
If anything, the opposite is true—most LLMs are trained on public data (including search engine sources), so you need a solid SEO strategy to maximize the amount of data coming from your website.
"The vast majority of the data used to train state-of-the-art LLMs are texts scraped from publicly available Internet resources"
– Xabier Lareo, Large language models (LLM) for European Data Protection
In-depth, comprehensive content signals to LLMs that your site is an authoritative source worth citing when users ask detailed questions on the topic.
This means that authority is as important as ever, so you need to showcase it through strong organic performance.
You should follow all the standard SEO best practices, including:
- Comprehensive keyword research and natural inclusion in content
- A solid internal linking strategy that lets search engines and LLMs pull relevant content quickly
- A strong backlink profile that signals authority
You should particularly focus on secondary keywords.
Identify and use semantic keyword variations naturally to boost topical relevance and demonstrate in-depth knowledge of a given topic.
You can turn to a site like Investopedia as a role model for outstanding SEO.
It consistently applies on-page SEO best practices and is cited widely in both search engines and AI-generated financial answers.
For example, a Google search for "what is a Roth IRA" will show Investopedia's page first due to the site's strong authority.
This authority translates into LLM visibility, so asking ChatGPT the same question will show Investopedia as one of the main sources.
6. Expand to digital PR
As LLMs scour the web for answers, you'll want them to stumble upon your brand as often as possible.
While SEO gets you there to an extent, you should also focus on digital PR that doesn't contribute to SEO performance directly but increases your brand presence and authority.
You can do this by boosting brand mentions, which is becoming increasingly important according to a recent study by Christina Blake and Nick Haigler.
The study showed that while search rankings play a major role in LLM visibility, PR efforts, industry forums and partnerships are also notable contributors worth exploring.
To increase brand mentions, you can take an active approach through press releases and brand collaborations.
Still, these are one-off efforts, so you should combine them with more long-term activities like publishing original research that can be cited by major publications (and LLMs as a result).
Create content that will be picked up by industry-specific publications (e.g., TechCrunch or Wired in the tech space), as well as universally authoritative sources like Forbes.
Such content can include:
- Internal studies or research
- Thought leadership pieces
- Industry reports
- Whitepapers and e-books
The perfect example of content that does wonders for digital PR is Calendly's 2024 State of Meetings Report.
It offers comprehensive insights, which attracted plenty of brand mentions and backlinks from authoritative sources like McKinsey and Deel.
As more publications cite Calendly's research, the brand's authority keeps increasing, which makes it more likely to be cited by AI responses.
7. Experiment with LLMs.txt
llms.txt is a proposed protocol (similar to robots.txt for search engines) aimed at guiding LLM crawlers on how to access and use site content.
It doesn't directly influence how AI platforms see your brand as a whole and what they remember about it in the long run, but it may help include it in individual responses.
The emphasis here is on "may" help—adoption of llms.txt is still early and inconsistent, but it doesn't hurt to be an early adopter and add the file to your pages.
According to research by the Developer Marketing Alliance, doing so resulted in various improvements, such as:
- Improved factual accuracy of AI responses
- Better relevance to the search query
- Improved response completeness
In other words, adding the llms.txt file helps guide LLMs to your content and may signal its relevance and credibility.
You might be wondering what the llms.txt file actually includes to provide LLMs with the necessary guidance.
While the complete answer can be pretty long, some of the key elements to focus on include:
- Allowed/disallowed paths
- Attribution requirements
- Contact info for API/content licensing
If this sounds a bit technical, you're right—llms.txt adoption does require some specialized knowledge, so it might take a while to do it right unless you're already familiar with it.
If you don't have the bandwidth to configure llms.txt yourself, get expert support to do it quickly and efficiently.
The sooner you do it, the better because OpenAI, Perplexity, and other AI platforms are exploring LLM-specific crawling rules, so early adopters might get a competitive edge.
How to track and measure LLM visibility
Much like you'd track traditional SEO performance, you should monitor your brand's mentions in LLMs. In fact, you can even use the same tools to do so—Google Analytics 4 offers a way to track traffic from LLMs through the Exploration report.
Here's how to set it up:
- Create a new report and set the dimensions to Session source/medium.
- Add Views, Engaged sessions, and Key events as metrics
- Create a new session segment and give it a descriptive name related to AI/LLMs
- Input the regex formula containing the LLMs you want to track
- For example, your formula can look like this:
- ^.*ai|.*\.openai.*|.*copilot.*|.*chatgpt.*|.*gemini.*|.*gpt.*|.*gemini.*google.*$
Make sure the regex formula includes all the LLMs you want to track, and update the formula as new AI tools surface.
You can also track brand mentions in AI content with Surfer's AI tracking tool.
- Enter your brand name and niche
- Surfer will generate a dashboard displaying your AI mentions in popular AI apps.
You'll also get opportunities that you can optimize for.
Key takeaways
- LLM optimization is the process of optimizing your content for conversational AI tools so that your brand gets featured or cited in their responses. The goal is to leverage LLMs as a new source of organic traffic to expand beyond traditional search engines.
- LLMs further promote the shift from keyword matching to natural language queries. You should optimize your content for question-based terms that users might type into AI chatbots. The content should answer the questions as directly and accurately as possible to get featured in AI responses.
- Entity optimization is essential to successful LLMO. Ensure NAP citation consistency across sources, and connect your brand to as many related queries as possible to maximize the chances of appearing in AI responses.
- LLMs pull information from search engines, so boosting your presence in Google's Knowledge Graph can help you show up in conversational AI. Besides consistent citations, you can achieve this by using schema correctly and claiming your Google Business Profile.
- Much like keyword research is crucial to successful SEO, understanding the questions users ask benefits your LLM optimization strategy. You can ask AI tools to suggest those questions, use third-party tools, or find questions in Google's People also ask section.
- LLMs often reference forums, Q&A websites, and other places with user-generated content. Get users to talk about your brand and solutions for a chance to get featured in the responses.
- Despite the rise of AI, SEO is as important as ever. Stick to SEO best practices to keep improving your online visibility and increasing your brand's credibility—doing so will help LLMs understand its relevance to user queries.
- For a more technical aspect of LLM optimization, use llms.txt to guide LLMs through your content and support quick access and analysis. We're likely to see LLM-specific crawling rules soon enough, so configuring llms.txt early can future-proof your content's visibility in AI tools.
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