SEO Strategy for LLM, Building Online Presence in the AI Search Era

These days, search engines are undergoing a major transition. Google remains a promising source of traffic. As a result, content optimization using SEO techniques involving keyword research, on-page SEO, and backlinks is still being carried out.

However, LLMs are emerging as “new players.” Many people are starting to switch to tools such as ChatGPT, Gemini, and Perplexity to find quick answers. However, this does not mean you have to eliminate SEO techniques and switch completely to LLM optimization.

Both can still be used together with some adjustments. So, what are the SEO strategies for LLM that need to be implemented to keep content relevant in the middle of this search trend shift?

This article will thoroughly explore LLM, starting from its definition, how it works, its comparison with traditional SEO, and SEO strategies for LLM. If you want your business to be “noticed” by LLM and exist in the digital competition arena, read the explanation until the end!

What Is LLM?

LLM stands for Large Language Model, which is an Artificial Intelligence (AI) system trained using large amounts of text data. This model is capable of understanding language patterns, interpreting meaning, summarizing, and generating answers that resemble human language. 

AI tools such as ChatGPT, Perplexity, and Claude utilize LLM to search for relevant content snippets from the web or other data sources, then compile them into answers that are easy for users to understand.

This means that AI tools will not view the entire web page, but only relevant content snippets that match the user's question. 

How Does LLM Work?

As mentioned earlier, LLMs use large amounts of text data to generate accurate and specific answers. This is possible because LLMs work according to the following scheme:

1. Learning Large Amounts of Text Data

In order to function, LLMs learn millions or even billions of pieces of text data from various sources, including news articles, online forums, books, and so on. 

The diversity of information presented in these sources makes it easier for LLMs to understand language patterns and the context of word usage. This data then becomes a concrete guide for LLMs when responding to user questions.

2. Predicting the Next Word

After understanding language patterns and context in text data, LLM works on the principle of predicting the next word. Here, the model will guess words that are most likely to appear after a certain word. 

For example, if you give the input “The sun is shining very ...”, LLM will predict the next words, such as ‘hot’ and “bright”. This can happen because the same patterns often appear in the text data that was studied previously.

3. Using Weight and Bias Parameters

LLMs have millions or even billions of parameters that regulate how they understand language. These parameters are divided into two types, namely weight and bias. 

Weight determines how strong the relationship between words is or how compatible one word is with another. For example, the word “rain” is more often associated with ‘heavy’ than with “drizzle.” Therefore, the weight of the “heavy-rain” combination will be higher.

Meanwhile, bias parameters help LLMs adjust their predictions to be more accurate, especially in specific contexts. These parameters enable LLMs to understand unique contexts, rather than just following common patterns.

Read also: New Era of SEO: Benefits, Tools, and AI SEO Features!

4. Supported by Transformer Architecture 

The architecture that supports LLM efficiency is the transformer architecture. With this architecture, LLM can understand word order and the connection between words in a sentence. 

Transformer architecture is based on artificial neural networks, making it more efficient than previous models that processed words one by one in sequence. The advantage of transformers is their ability to handle longer sentences in their entirety.

In the transformer architecture, there is a self-attention mechanism that helps LLM to calculate the relationship between each word and all the words in a sentence.

To understand this better, let's use the sentence “Meatballs are served in a bowl with dumplings on top.” The self-attention mechanism will help the LLM connect the word “meatballs” with “dumplings” even though they are separated by several words.

With this technology, LLMs have become increasingly level-up because they can capture meaning in longer formats, such as paragraphs or documents, without losing the overall meaning.

Traditional SEO vs. LLMO

When it comes to improving content visibility, one popular mechanism is SEO (Search Engine Optimization). SEO itself is closely related to content ranking on search engines.

However, with the rise of AI, increased content visibility is also supported by LLMO (Large Language Model Optimization). To avoid confusion between the two, consider the following differences between Traditional SEO and LLMO:

Read also: Google vs. GPT Search, Who Leads the Search Revolution?

Applying SEO Strategy for LLM

In the middle of the growing use of AI, LLM is considered more relevant than traditional SEO. However, if you have already applied SEO strategies to your previous content, there is no need to delete it and start from scratch.

You only need to adjust your SEO strategy to compete in the era of LLM-based search. Here are some AI search optimization techniques you can use:

1. Use Long-Tail and Conversational Keywords 

Humans often ask questions using conversational language. They are usually more comfortable writing questions such as “What sites can be used to create a free portfolio with no content restrictions?” rather than “Free portfolio sites.”

This can be facilitated by AI-based searches that use Natural Language Processing (NLP). NLP enables search engines to interact like humans.

LLM or AI-based search focuses more on answering questions like “how” and “why,” unlike traditional SEO, which focuses more on “what.” Therefore, you can adjust your keyword usage from short keywords to longer, conversational ones.

2. Provide Balanced and Comprehensive Answers

It is essential to recognize that LLM prefers content that is comprehensive, balanced, and incorporates multiple perspectives. Therefore, AI search optimization can be done by writing in-depth content that has the potential to answer all aspects of the user's question.

One way to do this is by presenting comparisons, alternatives, and unique perspectives. For example, when you create content comparing your product and a competitor's product, the advantages will certainly be mentioned. 

Next, if you want your content to be “noticed” by LLM, try to mention the disadvantages transparently as well. Although it seems counterintuitive to marketing, LLM considers this type of content to be more meaningful and comprehensive. This concept is in line with the principles of semantic SEO.

Content completeness is also closely related to user intent. Both SEO and LLMs consider user intent to be important. However, LLMs place greater emphasis on deep understanding by predicting follow-up questions.

For example, when discussing tablet recommendations for graphic designers, you should also include discussions about price, features, or how to choose the right tablet.

Read also: Google AI Overview and Its Struggle to Keep Up with Human Wits 

3. Diverse Content Formats and Platforms

You need to know that the wider the distribution of content and the more varied its formats, the greater the chance that AI will cite it as a reference. This is because AI does not only rely on data in the form of articles, but also on YouTube videos, LinkedIn posts, Reddit, Quora, and so on.

Therefore, you need to implement an omnichannel strategy, which involves distributing content across various channels. This allows your content to reach a diverse audience and increases the chances of AI finding it.

Some ways to do this include writing product reviews on Reddit, creating infographics for social media, or writing guest posts on other blogs.

4. Implement Technical SEO and Structured Data

In addition to paying attention to how you deliver content, you also need to ensure that your website is fast, clean, and easily accessible. This is because technical issues will make it difficult for AI to cite your content.

Some AI search optimization efforts you can make include improving pagespeed, using clear structures and appropriate schema markup, and not relying too much on JavaScript. 

5. Track AI Search Performance and Improve It

While traditional SEO evaluations use criteria such as ranking and organic traffic, LLM evaluations focus on how it mentions your brand, the context of the mention, and whether any sentiment is present.

To understand this, you can use supporting tools such as brand mention tracking and sentiment analysis reports. You also need to start measuring how much traffic comes from AI interactions, for example, using Semrush's Enterprise AIO.

However, do not stop at only tracking and analyzing! You can also improve it by optimizing old content, adding fresher articles, improving website technical aspects, or building collaborations with trusted media.

That is the SEO strategy for LLM that you can implement to increase brand visibility in the current era of AI search. Technology will continue to evolve, so make sure you do not miss out. Optimize your website consistently because that is your brand's ammunition in the digital world.

If you need help maximizing your website's potential to appear at the top of search results, or even become an AI Overview reference website, you can rely on cmlabs. By using data-driven strategies, cmlabs has successfully helped several clients from the healthcare and financial industries achieve high authority in search engines.

SEO Services by cmlabs offer resource-efficient solutions because you do not need to build your own in-house team. Additionally, this service is more cost-effective because the cmlabs team will help your brand build sustainable organic visibility.

Do not hesitate any longer! Contact our marketing team now for free, get an interesting offer, and be ready to appear better on search engines!

Read also: SEO Article Writing Experiment: AI vs Human