- By Ruchi Pardal
-
~ 12 minutes read
~ 12 minutes read The rapid evolution of artificial intelligence has opened a new era in the history of digital search, due to the Large Language Models (LLMs).
These very sophisticated AI models, such as GPT-4 and Google BERT models, have been trained on massive data to understand and generate very accurate human-like language.
So, what’s their impact?
Google’s search market share dropped below 90% in October 2024, the lowest since March 2015, as users increasingly turn to AI chat platforms like ChatGPT and Gemini for information
LLMs, which are based on natural language processing (NLP) and powered by transformer models, have changed how search engines interpret queries and deliver responses.
This trend is reshaping the search behavior of users and necessitating a significant shift in Search Engine Optimization (SEO) practices.
As search technologies are integrating LLMs, they also affect how information is accessed by the user and how companies can make their information visible.
This article examines the enormous impacts LLMs will have on search behaviour, the related adjustments to SEO tactics, and how businesses can adapt.
LLMs are transforming the dynamics of the user-search engine and AI-based tool interaction.
Just to give you an example, in health-related queries, traditional search engines correctly answer 50–70% of questions, often limited by irrelevant retrieval results, while LLMs achieve about 80% accuracy.
The key forms through which such changes are occurring are the following:
ChatGPT, or AI Overviews with LLMs, can provide simple and concise solutions to requests of users. Take the example of the question “What is blockchain?” that can provide a complete response without clicking to a site.
This aspect can reduce the traffic on a regular website when a user has the need of getting information since most of the answers are normally sufficient to acquire information through the AI interface.
However, the more complicated or action-oriented searches, such as purchase of products or access to extensive tutorials, will continue to drive people online, and it is therefore worthwhile that companies offer a unique value.
Conversational AI and voice search have boosted the number of natural questions. Nonetheless, the users can type the question “What is the best smartphone to buy this year?” instead of entering the phrase best smartphone 2023.
The LLMs can understand the context and intent of such conversational queries since they can perform delicate language processing.
This shift accelerates the content creators to produce more natural and question-oriented content that is in line with the existing user search behavior.
Personalization. The user data (search history or preferences) can be analyzed with the help of LLMs, and the results are personalized. To illustrate, a user who has already typed the query like healthy recipes can receive the suggestions of a diet or past queries.
This kind of personalization increases user satisfaction at the expense of the SEO professionals who are challenged to create extremely niche and, at the same time, generalizable content that is attractive to a large number of people.
LLMs alter search behavior with huge implications on SEO. The back links and keyword density tactics are old school and no longer sufficient.
The key areas where SEO will have to change are the following:
The LLMs pay more attention to the user intent instead of keywords. One such model is the BERT model developed by Google that knows the context of words in a query including the mechanism of prepositions or negations to deliver results that are more relevant in search.
SEO strategies must move toward creating content that can meet all the requirements of the user, answer questions, and provide added value that does not come down to simply optimizing keywords.
With structured data, such as schema markup, LLMs may understand the context and relationship of the content.
With the help of such schema as Organization, FAQPage, or Article, websites can provide information on the purpose and structure of their content, which would be simpler to decompose and refer to by AI.
This enhances searchability by both the traditional search engines and by the AI-based search tools.
LLMs like knowledgeable, authoritative, and reliable information (E-A-T).
The AI tool is more likely to cite high-quality content that is detailed in terms of information, originalities or peculiarities. Businesses must invest in the creation of credible, well researched content to keep themselves in the limelight.
New ranking factors are emerging, as the LLMs affect search algorithms. These are the depth of content, clarity and relevance with regards to user intent.
Also, the AI-enhanced functions, such as AI Overviews offered by Google, emphasize the structurally sound and regularly updated content, which makes SEO professionals agile and responsive to such alterations in behaviors.
To have a better understanding of how these LLMs have made a difference, it is beneficial to have an idea of their technicalities.
The main difference between LLMs and other models is that LLMs are constructed using transformer architectures that make them operate with language based on relationship analysis between words in big data. Important aspects obtained are:
During search, such search models as Google BERT and MUM (Multitask Unified Model) improve query comprehension. BERT is an example; it performs exceptionally in the interpretation of subtext, like when the word shoes is used with or without the word running.
MUM takes it further, realizing queries in multiple languages and formats, which allows a more detailed response. These innovations enable search engines to provide responses that match the user’s intentions in the best manner.
As there is scope to be even more advanced, the future of LLMs can be to enhance other new features to the search or add factors to ranks.
As an example, SEO strategies may further change as newer models are better at multimodal queries (a combination of text, images, or voice) or more easily combine real-time information.
In order to survive in the age of LLMs, SEO specialists should use an integrated strategy. The following are 14 doable strategies based on industry wisdom to get the best of traditional and AI-driven search:
The most crucial are the entities, such as brand names, locations, or product lines, that LLMs ought to recognize in your content.
Such tools as Natural Language API by Google or Content Editor offered by Surfer can be used to find suitable entities and incorporate them into the text in a natural way. Ensure Name, Address, Phone (NAP) data are uniform across the platforms.
Example: A local business should always submit a name and address in order to appear in the local suggestions generated by AI.
Add schema markup (e.g., JSON-LD for Organization or FAQPage) to give the LLMs context on the content. Google Search Console will help you have a Knowledge Graph on your site and gain credibility.
Example: To look in the Google Knowledge Panel, a medical practice can use schema marking to boost trust.
Write on something that can address commonly asked questions in your niche. Search for tools like AlsoAsked or AnswerThePublic that will assist in finding the most popular questions to deliver the content as clear and concise answers.
Example: A post on a cybersecurity blog that contains a separate section with the answer to the question What is malware? will be more likely on top of AI responses.
To generate authentic content, Foster uses reviews, forums or community conversations. The prompts that LLMs mention in regards to such platforms as Reddit or Stack Overflow are often commercial in nature.
Example: ChatGPT may refer to the brand of toothpaste which encourages the community of Reddit to discuss the fluoride-free toothpaste.
Continue keyword research, internal linking and back links. To enhance topical relevance, use secondary keywords. Ensure that there is strong technical SEO like speed and mobile friendliness.
e.g. The consistent position of financial terms in Investopedia is explained by powerful keyword strategies and internal linking.
Mention the brand more through press releases, partnerships, or original research. That will help your content seem more credible to LLMs when it gets referenced by trusted resources.
Think of the proposed llms.txt protocol to direct LLM crawlers and specify allowed paths and attribution preferences. The faster you implement AI, the more your content will be utilized by the tools.
Example: Developer Marketing Alliance research suggests that the accuracy of the content in the AI responses could be enhanced by the use of llms.txt.
Become the expert in a topic by including statistics, examples, or unique insights. Write in concrete terms and structured visuals like tables or figures.
An example: a technical blog with a lot of code examples and benchmarks may become a landmark in AI tools.
Write in a format that is easy to parse with good headings, bulleted lists, and short paragraphs. Avoid using heavy words or ambiguous words.
Example: AI will cite more often an article with H2/H3 titles and steps written in bullet points.
Post on websites like Reddit, GitHub, or LinkedIn to be cited and mentioned. Avoid paid links since this is not even genuine.
Example: A new business that has a case study on Hacker News may get more citations on AI.
Review or refresh at intervals of 30, 90, or 180 days to ensure that it is applicable. Fix broken links, update timestamps, and redirect old pages.
Ex. A blog maintaining a page of the Best tools in 2025 and changing it once a year will be applicable to AI.
Organize the data in a topical topic with subtopics developed and cross-linked articles. This generates authority of subjects.
An example of this is a SaaS organization that bundled the content around the term predictive maintenance and saw a massive growth in the traffic.
Provide original, measurable data like surveys or case studies. Use current information in tables or bullets to attract the focus of the LLM citations.
Examples: AI-generated snippets featured a proprietary survey-driven marketing guide.
Add schema markup, e.g. FAQ or HowTo, to the question-based content and it will be much easier to extract and quote by LLMs.
Example: WordPress site containing schema about FAQs appeared better in Google AI Overviews.
| Strategy | Key Actions | Tools/Resources |
| Optimize for Entities | Incorporate brand names, locations; ensure NAP consistency | Google Natural Language API, Surfer Content Editor, BrightLocal Citation Tracker |
| Build Trust with Structured Data | Use schema markup (Organization, FAQPage); verify Knowledge Graph presence | Schema.org, Google Search Console |
| Answer User Questions | Create Q&A content; research popular queries | AlsoAsked, AnswerThePublic |
| Encourage User-Generated Content | Foster reviews, forums; leverage platforms like Reddit | Reddit, Stack Overflow, Discord |
| Traditional SEO Best Practices | Conduct keyword research, internal linking, technical optimization | SurferSEO, Ahrefs, Google Search Console |
| Engage in Digital PR | Secure mentions via press releases, research | Press release platforms, industry collaborations |
| Experiment with llms.txt | Implement protocol to guide LLM crawlers | Developer Marketing Alliance research |
| Create In-Depth Content | Include metrics, case studies; use precise terminology | Internal research, industry benchmarks |
| Structure for Machine Readability | Use headings, bullet points; avoid dense text | Content management systems, SEO plugins |
| Promote in Communities | Share on Reddit, GitHub, LinkedIn; avoid paid links | Social media platforms, community forums |
| Regularly Update Content | Refresh every 30/90/180 days; fix 404s, update timestamps | Google Search Console, content audit tools |
| Holistic Topic Clustering | Group content around themes; interlink subtopics | SurferSEO, content planning tools |
| Embed Original Data | Include surveys, case studies; present clearly | Survey tools, data visualization platforms |
| Knowledge Base Markup | Use FAQ, HowTo schemas; test with validators | Schema.org, Google Structured Data Testing Tool |
Large Language Models are disrupting search and SEO because they are no longer relying on keyword-based search but instead on high-quality content based on intent.
Knowing about the effect of LLMs on search behavior and how to work around it with the help of strategies, companies can maintain and enhance their presence online.
These listed strategies including entity optimization and digital PR are the path to success in this AI-driven world. The desire to change and the commitment to quality will continue to make businesses successful as the search technology evolves.
Ready to navigate the AI-driven search landscape? Partner with Resultfirst to optimize your SEO strategy for Large Language Models.
Our expert team can help you create authoritative, intent-focused content that stands out in both traditional and AI-powered search.
Visit ResultFirst to get started today.
LLMs are AI systems trained to understand and generate human language. They impact SEO by prioritizing user intent, context, and content quality, requiring strategies that focus on comprehensive, authoritative content over keyword stuffing.
Optimize by creating intent-driven content, using schema markup, answering questions directly, and ensuring regular updates. Tools like AlsoAsked and Google’s Natural Language API can guide content creation.
Traditional SEO targets search engine rankings through keywords and backlinks, while LLM optimization focuses on discoverability across AI tools, emphasizing context, structure, and credibility.
Structured data, like schema markup, helps LLMs understand content context and relationships, improving the likelihood of being cited in AI-generated responses.
Monitor referrer traffic from AI platforms (e.g., ChatGPT, Perplexity), track citations in AI responses, and use tools like Google Search Console to assess search performance changes.
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