9.2
HomeBlogGeneral SEO informationHow does LLM tracking work?
Read more
Lennard Mulder
  • General SEO information
  • 5 minutes reading time

How does LLM tracking work?

The way people find information online is changing. Increasingly, they are asking questions to systems such as ChatGPT, Perplexity, or Gemini. These language models provide immediate answers, without the user having to click on a search result. For companies, this has important implications: visibility is shifting from traditional search results to answers generated by these models. LLM tracking is a way to keep track of this new form of visibility. In this article, you can read about what LLM tracking is, how it works, how reliable the results are, and how you can use these insights within a GEO strategy.

 

 

 

Inhoudsopgave

What is LLM tracking?

LLM tracking stands for Large Language Model tracking and focuses on measuring how and when your brand, product, or organization is mentioned in responses from language models. Think of a mention in ChatGPT’s AI Mode, a quote in Google’s AI Overviews, or a source citation within Perplexity.

Whereas traditional SEO mainly focuses on data relating to search positions and traffic, LLM tracking shows you the context in which your brand appears. It shows how language models interpret your organization, which themes are associated with it, and how often you appear in relevant responses.

A brand can be highly visible in AI responses without anyone actually clicking on the website. At the same time, a high ranking in Google does not guarantee that your brand will also be mentioned in these new search environments.

LLM tracking helps companies understand where they are mentioned, how they are positioned, and what that says about their digital authority.

 

 

 

How does LLM tracking work?

LLM tracking analyzes responses provided by large language models based on frequently asked questions or search queries. The goal is to gain insight into how your brand appears in these responses and what role it plays in them. The way this is done varies depending on the niche and target audience. Below, we explain the most important methods:

1

Measurements via simulated questions

LLM tracking tools perform tests at fixed intervals by asking sample questions to language models such as ChatGPT, Perplexity, Gemini, or Bing Copilot. The answers are stored and analyzed. This creates a dataset that reveals:

  • which brands or organizations are mentioned;
  • in what context this occurs (e.g., as a recommendation or comparison);
  • whether links, citations, or descriptions are present;
  • how consistently these mentions recur when the same question is repeated.
2

Context analysis and interpretation

It’s not just about whether a brand is mentioned, but especially how. Is your organization presented as an expert, supplier, or just one of several options? Analyzing these nuances provides a better picture of how language models understand and position your brand. This says a lot about the degree of digital authority that a model assigns to your organization.

3

Patterns and development over time

By performing the same tests regularly, you can identify trends. Is your brand mentioned more often, or less often? Do you come back to broader topics or only to very specific questions? This development shows whether your brand is gaining recognition within generative environments and/or whether optimizations are actually having an effect.

4

Signals and trends, not exact figures

LLM tracking provides signals, not hard statistics. It does not measure how many users ask a question or how often they click, but provides insight into visibility, context, and recognition. This is precisely why it is a valuable addition to traditional SEO data.

How reliable is the data?

LLM tracking offers valuable insights, but it is important to understand that this data and these insights are not exact. The results show patterns and signals, but can vary from moment to moment. This has to do with how language models work and how dynamic their responses are. There is also no clear insight into the exact use of prompts, which means that prompt investigations are based on prompts with the highest probability.

There are a number of factors that influence the reliability of LLM tracking data:

There is no measurable search volume.

Unlike traditional search engines, language models do not have search volume or click data. They do not release data about search queries or user interactions. Tools that claim to report “LLM search volume” or “prompt demand” base their findings on inaccurate estimates or model assumptions. LLM tracking therefore provides a qualitative picture of brand visibility, not a quantitative metric.

The answers vary depending on the user.

Language models generate responses based on context, wording, and sometimes even previous interactions. As a result, the same brand may be mentioned for one user but not for another. Responses can also change within a single session if the question is phrased slightly differently. This makes LLM tracking a snapshot in time: results can vary from test to test, even with identical prompts.

There is limited transparency about sources.

We often don’t know exactly what data a model uses to compile an answer. Some systems refer to specific sources, but most do not provide a complete overview of where the information comes from. Tools attempt to partially reconstruct this by linking URLs or recognizable text fragments to known websites, but this remains an approximation. It is therefore wise to interpret the results as indicators of visibility, not as conclusive evidence.

There are regular model updates

The models behind systems such as ChatGPT, Gemini, and Perplexity are regularly updated. New versions may contain different sources, use a broader knowledge window, or handle prompt structures differently. This means that a brand that is frequently mentioned today may suddenly become less visible after an update, even though nothing has changed on the website. Structural monitoring is therefore more important: the trend over time says more than a single result.

The power of interpretation

Ultimately, the value of LLM tracking lies not in the exact figures, but in the interpretation of the signals. It helps you discover how consistently your brand is recognized, what topics it is associated with, and whether its visibility is increasing or decreasing. By linking these insights to your existing SEO data, you can gain a more complete picture of your digital position within AI-driven environments.

LLM tracking is therefore a way to understand how language models see and present your brand and, based on that, to work more consciously on content, authority, and brand consistency within Generative Engine Optimization (GEO).

Why is LLM tracking valuable?

Although LLM tracking is not always an exact science, it helps organizations understand how language models perceive their brand and where there is room for improvement. It reveals whether a brand is presented as reliable and knowledgeable, but also whether a brand is hardly mentioned in relevant topics, for example.

Insight into brand visibility within AI responses

LLM tracking shows whether your brand appears in language model responses and how often that happens. Are you consistently mentioned in relation to important topics or only sporadically? This information reveals how firmly your brand is anchored within generative search experiences.

Understanding context and brand position

The value of LLM tracking lies primarily in the context of mentions. Are you mentioned as a reliable party? That insight helps strengthen your digital reputation and improve the way AI systems interpret your brand.

Discovering content opportunities

If your organization is missing from relevant answers, that is a sign to investigate the cause. Perhaps there is a lack of content on specific themes or entities.

By addressing these gaps in your content strategy, you can optimize more effectively and become visible on topics where there is still room for improvement.

Strengthening your GEO strategy

LLM tracking and GEO reinforce each other. The insights gained from tracking help determine which entities, topics, and sources influence how language models represent your brand. Based on this, you can:

  1. Expand your content with texts that AI systems understand better;
  2. Optimize structure and metadata so that entities are more easily recognized;
  3. Build partnerships and mentions that contribute to your authority within the broader semantic environment.

From insight to action

The power of LLM tracking lies in the structural application of the results.

By following trends, you can better substantiate where opportunities lie and which optimizations are effective. This allows you to develop a data-driven GEO strategy that is not based on assumptions, but on measurable signals of brand visibility and recognition.

Which metrics do you use for LLM tracking?

The available metrics vary per LLM tracker, and there is no general consensus yet on which metrics are the most important to use. In our opinion, LLM tracking involves a number of important metrics:

  • How often your brand is mentioned as a source.
  • The position of your link within the LLM responses (compared to your competitors).
  • How often your brand is mentioned.
  • How often competitors’ brands are mentioned.

When you have insights into the above data, you get a good idea of whether your brand is likely to be mentioned more often than your competitors.

The role within GEO

LLM tracking is playing an increasingly important role within Generative Engine Optimization (GEO). Whereas SEO focuses on findability within search engines, GEO focuses on recognition and presence within language models. LLM tracking shows how AI systems interpret, position, and present your brand within their responses.

For example, you can see whether your brand is presented as an expert or a reliable source of information. This knowledge makes it possible to work specifically on the way language models recognize and describe your organization.

In addition, LLM tracking makes it clear that online visibility is no longer solely dependent on clicks or traditional positions in search results. An organization can be visible in AI responses without a user going directly to the website. This requires a broader view of findability, in which not only traffic is central, but above all presence, recognition, and authority.

Combining insights from LLM tracking with existing SEO data creates a complete picture of a brand’s digital visibility. You not only understand where you are found, but also how AI systems communicate about you. LLM tracking helps organizations strengthen their brand in the world of generative search engines and stay prepared for the next step in the evolution of online search.

Getting started with LLM tracking within GEO

At SmartRanking, we work daily on the future of search engine optimization. That is why we have developed our own LLM tracking tool, built on our internal AI technology SmartAI. This allows us to accurately measure how brands perform within language models and generative search environments. Companies that want to start a GEO process can use this LLM tracker to gain insight into their visibility in AI responses and improve it in a targeted manner.

Would you like to know what LLM tracking and GEO can do for your organization? Feel free to contact us. Our team of specialists will be happy to work with you to develop a strategy that fits your brand, your goals, and the future of search engine optimization!

Also relevant

Our services

About search engine optimization Our vision
The most important SEO trends
General SEO information
The SEO trends in 2025