The SEO world is shifting at an unexpected rate, and much of that change is driven by large language models (LLMs) like GPT-4. These AI advancements undoubtedly open up a plethora of opportunities for businesses and content creators, but they also pose new challenges.
Zero-click searches dominate results, reducing organic traffic as users find answers directly through AI Overviews and rich results. The rise of AI-generated content has raised the bar for quality, with search engines penalizing generic or thin AI-driven material. Add in hyper-personalized search results tailored to individual users, and SEO professionals are left navigating a complex digital playing field.
So, it doesn’t come as a surprise that over 57% of professionals report an increase in competition in the SEO space due to AI involvement. However, don’t let that discourage you. With the right strategies, you can achieve success in SEO, even with LLMs in the picture.
The LLM Revolution: Why SEO Can’t Ignore It
Maybe a few years ago, when ChatGPT was first introduced by OpenAI, you could ignore LLMs. However, the growth of AI language models is exponential, and we’re now in a time where SEOs must acknowledge their influence and incorporate them into their strategies.
In fact, those who have embraced LLMs are beginning to see the hazards of not doing so. In an SEOFOMO survey, 39% of SEOs said they felt threatened by AI Overviews, while 34% said that the adoption of LLMs to search instead of Google was a threat to their SEO consulting efforts. These findings indicate that it’s about time SEOs start focusing on LLM SEO to be prepared for the future of search.
This approach is far more advanced and superior to the one where an algorithm would look for your string or words on a web page and show it to you regardless of the context. It’s more like having a conversation with a human, where you ask a question and get a direct answer.
The New SEO Paradigm: Adapting to LLMs
The SEO shift that we’ve explained above means that using the same old keyword optimization methods won’t work anymore and clinging to them can even harm your rankings. Google actively penalizes practices like keyword stuffing and over-optimization, as they detract from user experience and signal low-quality content. It’s time to move on to context-driven optimization. So, it’s keyword stuffing vs. intent-centric content, and the latter wins when it comes to LLM SEO.
But what does this mean for SEO strategies? Here are some actionable tactics that will help you adapt to this change.
- Intent-Based Keyword Research: Go beyond basic keyword lists. Use tools like Semrush to group keywords by search intent (informational, transactional, commercial, or navigational) and then center your content that directly addresses what users need at every stage of their journey.
- Optimize for Semantic Search: Structure your content with rich headings, modular content, FAQs, and internal links that provide clear, logical pathways for search engines to understand context and relationships.
- Focus on Topic Clusters: Build content hubs by linking related articles to a central pillar page. This helps demonstrate authority on a subject and supports semantic relevance.
- Focus on Personalization: Adapt your content to address specific audience segments, whether it’s based on demographics or interests. Again, Semrush can help identify groups that make up your audience. You can then direct your content towards their preferences.
- Create Human-Centric Content: Google wants you to write for humans and not search engines. Heed the advice and invest time in delivering original insights and solving real user problems.
“Make pages for users, not for search engines.” — Google, 2002
Our good advice then remains the same over two decades later. To succeed in Google Search, focus on people-first content.https://t.co/NaRQqb1SQx pic.twitter.com/bibv53icz9
— Google SearchLiaison (@searchliaison) July 6, 2023
Proven Tactics for LLM-Driven SEO Success
Since LLMs have been around for some time, professional SEOs have figured out some best practices for optimizing for them. Below, we simplify the concept behind these tactics and then go on to explain how to put them into practice.
Create Content That Ranks in LLM-Powered SERPs
If you’ve had some experience using LLMs for search, you’ll notice how they don’t always bring up the top results from the usual keyword-based search. This means that some of the content ranking for long-tail queries might not rank as well when LLMs apply.
The difference lies in the way content was written traditionally and how it has been created now. LLMs don’t go for keyword-stuffed articles. Instead, they tend to show well-researched content that meets user intent. Their advanced understanding of language allows them to identify meaningful and nuanced information, rewarding pages that answer user queries in detail.
The way you structure and present your content also matters. LLMs analyze how content is organized to determine its topical relevance. Clearly defined sections, logical headers, tables, tags, and structured lists help LLMs “understand” your content more effectively.
Also, these models look for well-connected content. The more association there is between the content sections, the more likely it is that your page benefits from LLM-driven SERPs. For example, subtopics and potential follow-up questions help build this relationship to give your content the depth that LLMs use to judge its topical authority.
Some tools, such as the Semrush SEO Content Template, can create this content connectedness right off the bat. It gives your content the theoretical groundwork for an LLM-boosted search result. Rather than focusing on just isolated keywords, it encourages a focus on topic clusters, natural language patterns, and intent-driven organization. This way, it ensures that your content speaks to the user’s needs in the same way an LLM interprets and matches intent.
A successful example of this tool’s effectiveness comes from Why SEO Serious, an international SEO agency. They used the SEO Content Template tool to create outlines for their client’s (a local bakery) website. Combined with insights and assistance from other Semrush tools, the agency was able to rank its client in the top 10 search results for multiple keywords. The content based on the intent-driven outline is also likely to appear in AI Overviews (not yet live for client keywords as of now).
For example, if you search for the best hotels in London for families with kids in an LLM, it may further show you related searches like ”family-friendly activities in London.” It’s likely that people who first search for hotels then go on to look for kid-friendly things to do in the area. If your content already includes some of these activities, an LLM is likely to show it as the result of the user’s query.
Financial Services: Building Trust and Accuracy with LLM SEO
Like in healthcare, businesses in the finance sector have to establish a certain level of trust and authority to be considered credible by people and search engines (traditional and LLMs). Besides long-tail queries and intent-driven content, financial services also have to optimize with two other tactics.
- Modular Content: While written content is great, you also need to include additional elements to improve your rankings. For example, include mortgage calculators, financial planning templates, or infographics in your content since these are modular elements that LLMs can use to further personalize the results for the user.
- Structured Data: Structured data helps LLMs understand your content to answer specific queries. Financial services can use structured data for content like pricing information, stock market updates, and exchange rates to improve its LLM SEO visibility.
Future-Proof Your SEO: How to Stay Ahead of LLMs
AI-driven changes, especially generative search results, demand a proactive mindset. Traditional SEO tactics alone are no longer sufficient; staying competitive requires an ongoing commitment to learning and adjusting.
SEO professionals need to shift their focus from static optimization to dynamic and responsive strategies. This means not only planning for now but also for the future. Predictive SEO can help you stay ahead of LLMs by anticipating user behavior and creating content that AI search engines are likely to show in the future; for instance, you can identify rising trends with tools like Google Trends and develop evergreen resources for recurring queries to maintain relevance as LLMs highlight new priorities.
In addition to predictive SEO, ongoing monitoring and analysis of AI-driven changes is imperative. For example, use Semrush’s On Page SEO Checker regularly to see how your LLM-optimized content is doing. Similarly, in the Position Tracking tool, focus on AI Overviews, People Also Ask, and featured snippets to ensure visibility in these prime real estate spots.
As new LLM-driven features continue to emerge, be prepared to learn and adapt to them quickly. Besides keeping up with industry news, you can also follow Google Search Liaison Danny Sullivan on X, where he regularly shares updates and insights on the search engine’s developments.
LLMs and the Future of SEO: A Recap
LLMs are not just transforming SEO but redefining it. By prioritizing user intent, contextual understanding, semantic relevance, and personalized search results, they have shifted the focus from traditional keyword-based strategies to more dynamic, value-driven approaches. Businesses that adapt by creating high-quality, semantically rich content and optimizing for evolving SERP features will position themselves as leaders going forward.
As we’ve explored, tools like Semrush can simplify this transition, offering robust insights and automation to streamline keyword research, content optimization, and performance tracking. You can even combine it with other AI tools to further enhance your LLM SEO strategy.
The bottom line is that SEO is now tied to AI and LLMs. So, the sooner you start adapting to these new technologies, the better equipped you will be to stay ahead.
Frequently Asked Questions
What is an LLM in SEO?
In SEO, a large language model (LLM) is an AI model like ChatGPT or Google Gemini that can process and generate natural language. These models themselves act as search engines, providing intent-based answers to searchers. They can also serve as automation tools for SEO tasks like content generation and optimization.
What is LLM optimization?
LLM optimization refers to fine-tuning large language models like ChatGPT to perform specific tasks better. In SEO, it could mean training LLMs to perform certain SEO tasks like SERP analysis. The goal is to make the AI more accurate and useful for whatever purpose you’re using it for. On the flip side, it may mean optimizing your content so that it appears in LLM-generated search results.
How to automate SEO and content tasks with LLMs?
You can use LLMs to generate content briefs, write meta descriptions, or create blog drafts that align with keywords. They’re also great for summarizing competitor research, analyzing search intent, brainstorming headline ideas, and generating insights from data. You may pair them with tools like Semrush to integrate them into your workflow for automating tasks like creating content clusters or backlink outreach.
How does LLM-based optimization differ from traditional SEO?
LLM-based optimization is more focused on understanding and generating human-like content, whereas traditional SEO is about manually optimizing elements like keywords, backlinks, and page structure. Unlike conventional SEO, where optimum keyword use would be fruitful, LLM optimization requires intent-based keyword use and direct, comprehensive content.
How can I structure my content for LLMs?
Here are some tips for structuring your content for LLMs:
- Use clear, keyword-rich headings (H1, H2) to guide the AI on what each section is about.
- Use intent-specific, long-tail keywords naturally throughout your content, ensuring they fit within the context of the topic.
- LLMs love structured data, so use bullet points or numbered lists to present key info clearly.
- Include Q&A-style sections to capture conversational intent, which LLMs handle well and are also more likely to show for voice search results.
How do LLM-based searches impact local SEO rankings?
For businesses that optimize their content for long-tail, local keywords, LLM-based searches can improve their local SEO rankings. These businesses can benefit by optimizing for more natural language searches and focusing on local content, like adding location-specific keywords or reviews. It makes local search results more dynamic and aligned with what users are actually asking.
Do LLMs consider customer reviews for ranking purposes in AI-generated search results?
LLMs can consider customer reviews when generating search results. Positive reviews and frequent mentions of relevant keywords can boost a business’s visibility in search results. While LLMs don’t directly “rank” reviews, they contribute to overall content understanding, meaning reviews can influence how content is perceived by AI search engines.
How can businesses optimize for AI-driven search or LLM-based search engines?
To optimize for LLM-based search engines, use conversational language in your content. As for keywords, think about how people actually search, i.e., using full questions or phrases instead of just single keywords. Also, organize content with clear headings, bullet points, and FAQ sections to help LLMs extract the most relevant information. You can also implement structured data to contextualize your content.
About the Author
Writer
Djanan Kasumovic, Head of Growth at Influencer Marketing Hub, drives innovation in digital marketing with expertise in AI marketing and AI influencer strategies. He is directly involved in shaping content strategies for top clients like Viral Nation, NeoReach, Brandwatch, and more, blending creative flair with data-driven insights to ensure people not only enjoy the content but leave a little smarter.
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