AI Prompt Tracking for Brand Reputation, Not Just Sales

Here’s something about AI prompt tracking that got me thinking. Is it just me, or does it seem like everyone has forgotten about the importance of reputation and jumped straight into the sales pitch?

Yes, AI recommendations and sales are tightly connected, but I have a feeling that through the simplification of the process of how LLMs decide which brand to recommend and which one to skip, we forgot that reputation will always be the side of the coin that can make or break your business deal at the end of the day, whether it’s your online or LLM reputation.

So, the punch line is that AI prompt tracking shouldn’t focus only on commercial prompts. You need to look beyond that, because when you see how LLMs describe your brand, you’ll get a clearer picture of why sales isn’t that great. You will know exactly what segments you need to improve.

But you can’t get it through a “What’s the best project management tool?” prompt. You can see the results of your reputation through that answer, but not the cause. And sometimes, that cause isn’t your SEO guy, but the product, price, safety, shipping, or some random small thing your product development team never noticed.

In this article, we’ll go through this together, starting with what marketers can measure with prompt tracking in generative AI, why most frameworks assume people are shopping, examples of reputation prompt types that are probably missing from your monitoring process, and how to start your prompt tracking and AI answers checking with Mentionlytics.

What Marketers Measure with Prompt Tracking in Generative AI Today

AI prompt tracking is the continuous monitoring of a specific set of prompts throughout LLMs and AI search engines, measuring how often, in which LLMs, and in which prompts your brand appears in the answers. You can do it manually (for a lower volume of prompts or for one specific LLM, only ChatGPT, for example) or through an AI visibility tool. The prompt set focuses on the questions your target audience might ask.

As you can see, this definition differs from the classic dev definition of LLM prompt tracking because it focuses more on the marketers’ and generative engine optimization (GEO) purpose. If you ask a developer to explain prompt tracking, they’ll probably give you the backend version of the same story: tracking prompt versions, token cost, specific parameters (such as temperature), and latency.

So, what do marketers measure when they conduct AI prompt tracking? The list isn’t as long as you might think. You can track:

• Whether AI recommends your brand for relevant products, services, or use cases.
• Whether specific LLMs recommend your brand.
• What sources AI uses for building its list of recommendations.
• If LLMs use your content as citations.
• Which prompts your brand shows up in the answers.
• How prominently and accurately your brand is presented.
• Which competitors appear instead.
• What sentiment or context surrounds the AI-generated mention.
• Which sources influence the answer for a specific prompt.
• What are the dominant domains LLMs rely on when providing the answers.
• How these results change over time.

Keyword Tracking vs Prompt Tracking 

Though it might look as if these two processes have the same main goal, a.k.a. how well your content or your brand is visible for a specific keyword, phrase, or question, prompt and keyword tracking differ in length and style, search intent, insights, ranking environment, metrics, and how the ranking system works.

Why Do Most Prompt Tracking Frameworks Assume People Are Shopping?

Here we come to the point where most brands take the shortcut, measuring and tracking only recommendation prompts, which users with high shopping intent run. But the truth is that people don’t use LLMs only when they’re shopping. People also use Gemini, AI Mode, or any other AI model in every other phase of the customer journey.

Why do we lean towards recommendation prompts? Probably because of the statistics that hit us in the face at the very beginning of the AI search era, showing us that high-intent buyers come from LLMs, or that recommendations have an 18% conversion rate. Or simply because we inherited traditional SEO patterns where keyword research is mostly organized around commercial intent, and the vendors writing them mostly sell to B2B SaaS and ecommerce.

That’s true, but somehow we forgot that not all of ChatGPT’s 2.5 billion prompts a day are that direct or shopping-focused. Sometimes several informational prompts build up the buying intent.

In fact, in September 2025, the US National Bureau of Economic Research published a research paper on how people use ChatGPT, and the stats might make you rethink your prompt-tracking strategy:

• Inquiries about products or services available to purchase were 2.1%.
• Requests for factual information were 18.3%.
• Guidance on how to do something was 8.5%.
• Explanations and help understanding subjects were 10.2%.

Now, when we look at these stats, we can conclude that only 2.1% use prompts like “What is the best laptop for writing under $1000“, but we mustn’t forget that these 18.3%, 8.5%, and 10.2% of informational and instructional prompts also impact buying decisions.

So the most valuable insights from LLM visibility tracking come when you group prompts by intent:

• Informational: Users want to understand a topic or problem, e.g., “What’s the difference between dry and dehydrated skin?”
• Comparative: They’re weighing different options, e.g., “How does CeraVe’s moisturizing cream compare with La Roche-Posay’s?”
• Instructional: They need guidance on completing a task, e.g., “How do I introduce retinol into my skincare routine?”
• Brand-specific: They’re researching a particular brand’s products, reputation, or suitability, e.g., “Is The Ordinary’s niacinamide serum suitable for sensitive skin?”
• Transactional: They’re ready to take action, e.g., “Where can I buy CeraVe moisturizing cream in London?”

And every category is one step in the purchase journey: problem, exploration, comparison, validation, selection, and they all affect the final phase.

Let’s take, for example, an airline company. If you put safety first, no matter how often Google AI Overviews or Gemini recommends one airline company, you won’t follow the recommendation if the prompt about safety doesn’t confirm that the same brand is on the list. And that’s the power of informational prompts.

5 Reputation Prompt Types Missing From Your AI Prompt Monitoring Process

Five reputation prompts can help you find out (first-hand) what LLMs say about your brand, which might affect the purchase intent of your potential buyers.

Risk and legitimacy prompts

In the pre-LLM era, these prompts were a forum question. They cover safety concerns, trust, and whether the brand is legit or gives a fraud vibe. You want to know what LLMs answer to these types of questions, because they might be a deal-breaker for your next sale.

Tracking search queries like “Is XX safe to use”, “Is XY a scam”, “Has YY ever been fined?”, “Was XYZ brand involved in XX incident” across AI platforms can signal if something about your brand is presented the wrong way to the audience.

Depending on the specific query and industry, sources for these types of prompts can vary from news headlines, forums, groups, review platforms, corporate websites, blogs, and databases.

But the biggest threat here comes from outdated content: a forum discussion from two years ago where the problem might have been solved, but the AI platform pulls it and presents it as a current problem.

For example, I track Ryanair through Mentionlytics, and there are two prompts related to safety:

• “Is Ryanair safe and reliable?“, where all platforms describe it as a safe option with neutral and slightly positive sentiment in 99% of the answers. Perplexity describes it as very safe, with no fatal incidents, following all European safety standards, but points out that it’s one of the most delayed airlines worldwide (based on the 2025 flight tracking platform, Flighty, analysis).

• But when you check the second safety prompt, “Safest airlines in Europe”, Ryanair shows up on the list only in almost 62% of the answers.

Eligibility and entitlement prompts

These kinds of queries are typically run by existing customers, clients, or buyers who want a refund or compensation, and they check AI responses to see whether they are eligible.

For answering prompts like “Am I owed compensation from (brand)?”, “Does (brand) cover (situation)?”, “What are my rights if (brand) cancels?”, “Can I get a refund from (brand)?” or similar, AI platforms will check consumer rights sites, legal blogs, help forums, and often not your own documentation and help center.

These prompts should be on your list because if AI gives customers incorrect information about your policies or their rights, your support team may receive more questions and complaints. When declined, customers will continue to complain online about that, and you can end up with a serious reputation crisis. It is especially important in industries like banking or insurance, where these mistakes could also create legal or regulatory risks.

Ownership and factual prompts

Prompts like “Who owns (brand)?”, “Is (brand) still in business?“, “Where is (brand) headquartered?”, “Is (brand) the same company as (other brand)?” people typically use when they want to check the legal side of the business, or they want to see who stands behind the brand.

AI answers are typically sourced from Wikipedia, Crunchbase, TechCrunch, news, and company registries, and these are the prompts where stale data is most common and easiest to fix.

Incidental example prompts

These are prompts where your brand appears as an illustration rather than an option. For example, someone asks, “Which airlines are criticized for extra fees?”. If you’re an airline company and you already know through listening to social media conversations that customers are complaining about it online, that would be an incidental prompt I would include.

Or, for example, if your company previously disclosed and resolved a data breach, you might want to track the “examples of companies that had a data breach” prompt to discover whether AI accurately describes what happened and when, or makes an old incident sound like a current security problem.

When answering these prompts, AI platforms may draw on news articles, industry reports, case studies, forums, and reviews. But answers can also rely on information learned during training, so you won’t always have a citation to investigate.

Employer and workplace prompts

How you treat employees can influence whether people want to buy from you, work with you, or recommend you. If AI repeatedly describes your company as a toxic workplace or an employer with unfair practices, those claims can undermine your public commitments to fairness and responsibility.

Customers don’t have to apply for a job to care about how you treat the people doing it, because from their point of view, if you treat your employees as replaceable, that attitude might just reflect to customers.

So, if you want to see how AI describes your working environment and company culture, especially if you have a B2B brand, track prompts like: “Is (brand) a good place to work?”, “Did (brand) have layoffs?”, “What do employees say about (brand)?”

These answers are sourced almost entirely from Glassdoor, Reddit, Blind, and news.