Why LLM Monitoring Isn’t Actionable - Otterly.AI Blog - Best AI Search Monitoring Solution

Yes, you read that right.

Large Language Model (LLM) Monitoring isn’t actionable, and LLM Optimization (LLMO or LLM SEO) isn’t a real thing.

Hi, I’m Thomas, CEO and Co-Founder of OtterlyAI. Today, I want to address a topic that’s been on my mind for some time now. Bear with me – my goal here is to shed light on what I believe we, as an industry, are currently misunderstanding.

I get it. Whenever there’s a major disruption in any field, we – “the industry” – scramble to define it. We coin new buzzwords, form different schools of thought, and inevitably spark debates among the various camps with opposing views. That’s fine, and to be clear, I’m not here to dissect or argue about that process.

In the end, it doesn’t really matter to me what we end up calling this “new thing.” Whether it’s GEO (Generative Engine Optimization), AIO (AI Search Optimization), or AEO (Answer Engine Optimization), the terminology is secondary.

But I do have one simple request for you – yes, you, the SEO expert or marketer exploring this space.

Can we all agree that this isn’t about LLMO, LLM SEO, or any other acronym tied to LLMs in general?

Let me explain.

Our audience doesn’t use LLMs directly. They rely on AI Search and Answer tools.

Here’s the thing: as marketers, we don’t need to stress about Large Language Model (LLM) Monitoring or Optimization. Why? Because our audience—consumers—doesn’t interact with LLMs directly. Instead, they use search engines, which are evolving from traditional platforms (like Google) into AI-powered search tools (such as ChatGPT, Google AI Overviews, or AI Modes).

We do not use LLMs.

While it’s true that LLMs are the backbone of these AI search engines, our interaction is not with the models themselves but with user-friendly interfaces. These interfaces can be broken down, in simple terms, like this:

ChatGPT Interface = Large Language Model + Web Search + Personalization

1. Large Language Model (LLM)

At the heart of ChatGPT is a Large Language Model, trained on massive volumes of text from books, websites, and other written material. It learns:

  • How language works (grammar, tone, structure),
  • What concepts mean (e.g., what “product-market fit” or “churn rate” refers to),
  • How people typically phrase questions and answers.

How it works:

  • It doesn’t “know” facts the way a database does.
  • Instead, it predicts the next word in a sentence based on your input, using probabilities learned during training.

2. Web Search

Most LLMs (eg those from ChatGPT) are trained on data up until a certain date (known as knowledge cutoff dates). To stay current:

  • Web tools can be added to allow ChatGPT to fetch recent information from the internet in real time.
  • This is similar to plugging in a research assistant who quickly googles for the latest data, news, or product changes.

For example:
If you ask “What’s new in B2B SaaS marketing trends in 2025?”, ChatGPT may use live search to pull recent articles or event recaps—like from SaaStr or LinkedIn posts—before answering.

3. Personalized Context – Memory & Session Awareness

For a more useful experience, ChatGPT can use context from:

  • The current conversation (session awareness),
  • Persistent memory (optional, if enabled—you control this),
  • User-provided data, like “I work in B2B SaaS,” or “I’m planning an event.”

This lets it:

  • Tailor responses to your industry or company size,
  • Use past interactions to avoid repetition,
  • Offer more strategic suggestions instead of just generic tips.

Example:
If you often ask about SaaS content marketing, it will shift responses to be more strategic and B2B-focused—less fluff, more frameworks.

In summary: As a user, when I engage with platforms like ChatGPT.com, Google.com, or gemini.google.com, I’m not directly interacting with a large language model (LLM). Instead, I’m using an AI-powered search interface, which relies on an LLM among other technologies.

So, what’s the issue with LLM monitoring?

LLM Monitoring Tools Focus Solely on LLM Output

If you revisit the visual, you’ll notice that LLM monitoring tools address just a single aspect of the entire process. These tools are designed primarily to track and evaluate the output or response generated by a Large Language Model.

Why is this important?

  • Using an LLM monitoring tool ensures you understand how LLMs “perceive” and represent your brand.
  • However, this doesn’t account for what your audience might encounter when exploring platforms like chatgpt.com, perplexity.ai, or similar AI engines.

UI vs. API Output: A Closer Examination

Large Language Model (LLM) monitoring tools rely on API access, which often produces different outputs compared to AI search interfaces.

When you interact with AI through user interfaces like ChatGPT.com or Perplexity.ai, the responses you receive—complete with features like web citations—may not perfectly match the output from the corresponding API. Why does this happen? The explanation is straightforward: APIs offer developers greater flexibility and control over the settings, enabling them to customize behavior in ways that aren’t available to typical users interacting via the UI.

For example, platforms like OpenAI or Perplexity.ai let developers use APIs to build powerful software, adjusting the settings to fit their specific needs. However, LLM Monitoring Tools often use these APIs to imitate how users interact with interfaces like ChatGPT.com. While this method tries to copy the user experience, the outcomes aren’t always the same because APIs can be customized in different ways.

This distinction is so significant that Perplexity.ai explicitly addresses it in their FAQ section.

Gemini LLMs powering AI Mode, AI Overviews and Gemini.Google.Com

With Google’s ongoing rollout of various AI-powered Search Experiences, I wanted to take a moment to provide a clear summary of how these features differ.

Isn’t it interesting how Google states it’s a “custom version of Gemini 2.5”, without going into further detail what “custom” really means. It definitely means that it’s not the “default” Gemini LLM used.

AI Overviews integrate AI-powered responses directly into search results, enabling users to access information more quickly and seamlessly. In contrast, Gemini.Google.com offers advanced web browsing capabilities designed for more dynamic and personalized research, allowing users to fulfill specific requests within an ongoing conversation.

LLM monitoring alone isn’t the key to achieving greater AI search visibility.

Let me clarify—I’m not opposed to LLM monitoring. In fact, I believe it’s important for all of us to align on some fundamental principles. For me, the first principle is this.

What LLM Monitoring Can be Used For

LLM monitoring is a valuable tool for gaining insights into how an LLM “perceives” or processes information about a brand.

When LLM Monitoring Is Useful: Brand safety, hallucination detection, internal testing, and tone-of-voice consistency.

However, it’s not particularly effective when it comes to understanding how a brand appears in AI-driven search results. As a result, it doesn’t serve as a strong foundation for optimizing visibility on platforms like ChatGPT.com.

LLM Monitoring vs AI Search Monitoring

LLM Monitoring has its place – but it’s not the answer for marketers trying to boost visibility in AI Search Engines. That’s because consumers don’t interact with raw LLMs. They engage with search interfaces layered with real-time data, personalization, and UX decisions that shape the final output.

How We Think About This at OtterlyAI

Today, I want to reflect on something that dates back to 2024, when we introduced the very first version of OtterlyAI. To be completely transparent, our initial product was built around LLM Monitoring. We focused on tracking the output generated by the various LLMs available at the time.

However, after talking to hundreds of marketing teams, we quickly realized two key things: 1) the output from LLMs is distinct from real-user AI Search output, and 2) marketing and SEO professionals needed a more effective way to monitor and optimize their efforts. That’s when we pivoted and re-launched OtterlyAI in its current form. Today, it serves as an AI Search Monitoring solution, specifically designed to replicate and analyze the interfaces and outputs that our consumers engage with.

Ultimately, there’s no definitive “right” or “wrong” approach here—as I’ve emphasized multiple times in this discussion. That said, I believe it’s important to understand the different implications of focusing on LLM Monitoring versus AI Search Monitoring.

Because: The future of SEO isn’t about optimizing for the model. It’s about optimizing for the interface.

P.S. As you can probably tell, I’m deeply passionate about this subject. Don’t hesitate to reach out via email (thomas.peham@otterly.ai) if you’d like to have a more in-depth conversation!

Related Posts:

Yes, you read that right.

Large Language Model (LLM) Monitoring isn’t actionable, and LLM Optimization (LLMO or LLM SEO) isn’t a real thing.

Hi, I’m Thomas, CEO and Co-Founder of OtterlyAI. Today, I want to address a topic that’s been on my mind for some time now. Bear with me – my goal here is to shed light on what I believe we, as an industry, are currently misunderstanding.

I get it. Whenever there’s a major disruption in any field, we – “the industry” – scramble to define it. We coin new buzzwords, form different schools of thought, and inevitably spark debates among the various camps with opposing views. That’s fine, and to be clear, I’m not here to dissect or argue about that process.

In the end, it doesn’t really matter to me what we end up calling this “new thing.” Whether it’s GEO (Generative Engine Optimization), AIO (AI Search Optimization), or AEO (Answer Engine Optimization), the terminology is secondary.

But I do have one simple request for you – yes, you, the SEO expert or marketer exploring this space.

Can we all agree that this isn’t about LLMO, LLM SEO, or any other acronym tied to LLMs in general?

Let me explain.

Our audience doesn’t use LLMs directly. They rely on AI Search and Answer tools.

Here’s the thing: as marketers, we don’t need to stress about Large Language Model (LLM) Monitoring or Optimization. Why? Because our audience—consumers—doesn’t interact with LLMs directly. Instead, they use search engines, which are evolving from traditional platforms (like Google) into AI-powered search tools (such as ChatGPT, Google AI Overviews, or AI Modes).

We do not use LLMs.

While it’s true that LLMs are the backbone of these AI search engines, our interaction is not with the models themselves but with user-friendly interfaces. These interfaces can be broken down, in simple terms, like this:

ChatGPT Interface = Large Language Model + Web Search + Personalization

1. Large Language Model (LLM)

At the heart of ChatGPT is a Large Language Model, trained on massive volumes of text from books, websites, and other written material. It learns:

  • How language works (grammar, tone, structure),
  • What concepts mean (e.g., what “product-market fit” or “churn rate” refers to),
  • How people typically phrase questions and answers.

How it works:

  • It doesn’t “know” facts the way a database does.
  • Instead, it predicts the next word in a sentence based on your input, using probabilities learned during training.

2. Web Search

Most LLMs (eg those from ChatGPT) are trained on data up until a certain date (known as knowledge cutoff dates). To stay current:

  • Web tools can be added to allow ChatGPT to fetch recent information from the internet in real time.
  • This is similar to plugging in a research assistant who quickly googles for the latest data, news, or product changes.

For example:
If you ask “What’s new in B2B SaaS marketing trends in 2025?”, ChatGPT may use live search to pull recent articles or event recaps—like from SaaStr or LinkedIn posts—before answering.

3. Personalized Context – Memory & Session Awareness

For a more useful experience, ChatGPT can use context from:

  • The current conversation (session awareness),
  • Persistent memory (optional, if enabled—you control this),
  • User-provided data, like “I work in B2B SaaS,” or “I’m planning an event.”

This lets it:

  • Tailor responses to your industry or company size,
  • Use past interactions to avoid repetition,
  • Offer more strategic suggestions instead of just generic tips.

Example:
If you often ask about SaaS content marketing, it will shift responses to be more strategic and B2B-focused—less fluff, more frameworks.

In summary: As a user, when I engage with platforms like ChatGPT.com, Google.com, or gemini.google.com, I’m not directly interacting with a large language model (LLM). Instead, I’m using an AI-powered search interface, which relies on an LLM among other technologies.

So, what’s the issue with LLM monitoring?

LLM Monitoring Tools Focus Solely on LLM Output

If you revisit the visual, you’ll notice that LLM monitoring tools address just a single aspect of the entire process. These tools are designed primarily to track and evaluate the output or response generated by a Large Language Model.

Why is this important?

  • Using an LLM monitoring tool ensures you understand how LLMs “perceive” and represent your brand.
  • However, this doesn’t account for what your audience might encounter when exploring platforms like chatgpt.com, perplexity.ai, or similar AI engines.

UI vs. API Output: A Closer Examination

Large Language Model (LLM) monitoring tools rely on API access, which often produces different outputs compared to AI search interfaces.

When you interact with AI through user interfaces like ChatGPT.com or Perplexity.ai, the responses you receive—complete with features like web citations—may not perfectly match the output from the corresponding API. Why does this happen? The explanation is straightforward: APIs offer developers greater flexibility and control over the settings, enabling them to customize behavior in ways that aren’t available to typical users interacting via the UI.

For example, platforms like OpenAI or Perplexity.ai let developers use APIs to build powerful software, adjusting the settings to fit their specific needs. However, LLM Monitoring Tools often use these APIs to imitate how users interact with interfaces like ChatGPT.com. While this method tries to copy the user experience, the outcomes aren’t always the same because APIs can be customized in different ways.

This distinction is so significant that Perplexity.ai explicitly addresses it in their FAQ section.

Gemini LLMs powering AI Mode, AI Overviews and Gemini.Google.Com

With Google’s ongoing rollout of various AI-powered Search Experiences, I wanted to take a moment to provide a clear summary of how these features differ.

Isn’t it interesting how Google states it’s a “custom version of Gemini 2.5”, without going into further detail what “custom” really means. It definitely means that it’s not the “default” Gemini LLM used.

AI Overviews integrate AI-powered responses directly into search results, enabling users to access information more quickly and seamlessly. In contrast, Gemini.Google.com offers advanced web browsing capabilities designed for more dynamic and personalized research, allowing users to fulfill specific requests within an ongoing conversation.

LLM monitoring alone isn’t the key to achieving greater AI search visibility.

Let me clarify—I’m not opposed to LLM monitoring. In fact, I believe it’s important for all of us to align on some fundamental principles. For me, the first principle is this.

What LLM Monitoring Can be Used For

LLM monitoring is a valuable tool for gaining insights into how an LLM “perceives” or processes information about a brand.

When LLM Monitoring Is Useful: Brand safety, hallucination detection, internal testing, and tone-of-voice consistency.

However, it’s not particularly effective when it comes to understanding how a brand appears in AI-driven search results. As a result, it doesn’t serve as a strong foundation for optimizing visibility on platforms like ChatGPT.com.

LLM Monitoring vs AI Search Monitoring

LLM Monitoring has its place – but it’s not the answer for marketers trying to boost visibility in AI Search Engines. That’s because consumers don’t interact with raw LLMs. They engage with search interfaces layered with real-time data, personalization, and UX decisions that shape the final output.

How We Think About This at OtterlyAI

Today, I want to reflect on something that dates back to 2024, when we introduced the very first version of OtterlyAI. To be completely transparent, our initial product was built around LLM Monitoring. We focused on tracking the output generated by the various LLMs available at the time.

However, after talking to hundreds of marketing teams, we quickly realized two key things: 1) the output from LLMs is distinct from real-user AI Search output, and 2) marketing and SEO professionals needed a more effective way to monitor and optimize their efforts. That’s when we pivoted and re-launched OtterlyAI in its current form. Today, it serves as an AI Search Monitoring solution, specifically designed to replicate and analyze the interfaces and outputs that our consumers engage with.

Ultimately, there’s no definitive “right” or “wrong” approach here—as I’ve emphasized multiple times in this discussion. That said, I believe it’s important to understand the different implications of focusing on LLM Monitoring versus AI Search Monitoring.

Because: The future of SEO isn’t about optimizing for the model. It’s about optimizing for the interface.

P.S. As you can probably tell, I’m deeply passionate about this subject. Don’t hesitate to reach out via email (thomas.peham@otterly.ai) if you’d like to have a more in-depth conversation!

Related Posts: