Ultimate guide to tracking brand sentiment in LLMs - Peec AI

Ultimate guide to tracking brand sentiment in LLMs

Tomek Rudzki

GEO Expert

Jan 23, 2026

GEO Know-How

Summarize with AI

Most people tracking their brand in AI search focus on high-volume prompts like "best CRM software" and call it done.

But they miss the brand evaluation prompts that actually determine whether someone buys from you or walks away.

When a prospect is ready to purchase and asks "Is HubSpot easy to use?" or "Does HubSpot have good customer support?" That's a $50,000 annual contract hanging in the balance. These brand evaluation prompts protect individual sales while also shaping how AI understands your brand across every recommendation it makes.

In this guide, you'll learn how to track sentiment systematically, identify which areas are hurting you, and fix the underlying issues that make AI warn prospects away from your brand.

Why sentiment tracking changes everything

Let me show you how this plays out in practice:

Let's say you're tracking HubSpot's visibility in AI search and it's performing well. When people ask "best CRM software," LLMs recommend HubSpot regularly.

But what happens when that same prospect needs to confirm their choice right before buying?

In traditional SEO, these are evaluation stage queries with low search volume. But in AI search, they reveal what AI actually thinks about your brand.

I call these sentiment prompts because they expose AI's opinion of your brand across key attributes like support, usability, and pricing.

When you improve how AI perceives HubSpot's customer support, you get a multiplier effect. AI becomes more likely to recommend HubSpot in high-volume prompts like "best CRM for small business" because strong support becomes part of how it understands your brand overall. Tools like Peec AI let you measure this sentiment across different attributes and track how changes affect your overall visibility.

Part 1: How to Set Up Sentiment Tracking

Now that you understand why sentiment tracking matters, let's put this into practice for your brand.

Setting this up takes about 10-15 minutes. You’ll have three steps to complete: identify your brand sentiment themes, create prompts for each one, and organize each in a separate Peec AI project.

Step 1: Identify your sentiment themes

Sentiment themes are the specific aspects of your brand that people evaluate when deciding whether to buy from you.

Think about what questions your prospects ask before purchasing. For a CRM, that might be ease of use, customer support, pricing, integrations, reliability. For a bank, it's safety, convenience, and legitimacy, and so on.

Here's how I approached this for Revolut (an online bank), and how you can do it for your brand. Identify features and the core questions for each:

Your sentiment themes will be different based on your industry and common objections your customers have.

Step 2: Create prompts for each sentiment theme

Now that you have your sentiment themes, you need to create 10-20 questions for each one. These are the actual prompts you'll track in Peec AI.

The idea behind this is simple.

For the "Safety" theme, you might track questions like:

For "Customer support," you might track:

You can use LLMs like Claude to help generate these. I prefer Claude because its language feels more natural than ChatGPT, but most LLMs will help you do this.

Advanced method: You can add customer context to these prompts (like "Is Revolut safe for small businesses?" or "Is Revolut good for freelancers?"). I explain this approach in detail in How to choose the right prompts for LLM tracking.

But for sentiment tracking, start with the basic method first. Get the foundation working, then add customer context variations later if needed.

Traditional prompts vs sentiment prompts

If you’ve read my separate guide on Choosing the Right AI Search Prompts, you’ll notice that sentiment prompts look a bit different from your typical visibility strategy.

In a standard visibility project, you use non-branded prompts - like "best project management software"

But for sentiment tracking, we intentionally use branded prompts.

By putting your company name directly in the question (e.g., "Is [Brand] easy to set up?"), we’re not testing if the AI knows you exist. We are forcing the AI to talk about you so we can extract exactly what it says about your brand.

Step 3: Create a new project

Once you have your prompts organized by themes, create a dedicated sentiment tracking project in Peec AI. Remember to keep this separate from your visibility tracking project.

Now add your prompts organized by tags. This lets you analyze sentiment by themes quickly.

Part 2: Analyzing your sentiment data

Once Peec AI collects data (usually within a few minutes), you'll see patterns in your dashboard that would be impossible to catch manually. Here's where it gets really powerful: you can combine filters to uncover patterns you'd never see in aggregate data.

Find your worst-performing areas in under a minute

Open your sentiment project in Peec AI and filter by tags. Click on the first tag and check the sentiment score for that specific sentiment theme. Then, do the same for other tags one by one.

This quickly reveals your problem areas. In the Revolut example, the worst performers were:

That's your priority list. Start with the theme showing the worst sentiment.

Dig deeper with advanced filtering

Now you can group filters to find the root cause for lower sentiment scores:

Read the actual responses

Click into any prompt in your dashboard to see what LLMs actually say. Peec AI highlights where your brand is mentioned and whether it's positive, negative, or neutral.

This is where you find the exact language AI uses to describe your brand, such as "Users report slow or generic responses from customer support" or "Quick resolutions on common issues." When these phrases appear repeatedly, you know the narrative you're fighting.

Part 3: Five tactics to improve your sentiment

Now that you know which themes have the worst sentiment, it's time to find out why and fix the underlying issues.

You have five different approaches. Pick your weakest sentiment theme from the dashboard, then use these tactics to trace the problem back to its source.

Tactic 1: Fix inaccurate influential sources

Let's say "Safety" is your problem area. Go to the Sources tab in Peec AI, filter by the "Safety" tag, and sort by Used.

You're looking for outdated or inaccurate articles that LLMs reference repeatedly.

The fix is to politely ask these sources to update their information.

Tactic 2: Identify review platform outliers

A pattern I see constantly is companies checking their scores on major platforms like Glassdoor, G2, and Trustpilot and thinking they're fine as long as they have fairly positive ratings.

But LLMs often cite obscure review platforms that most companies never monitor. If you find evidence of coordinated attacks, follow steps to document patterns and contact the platform's trust and safety team.

Tactic 3: Update your own most influential URLs

In Peec AI, go to Sources and filter to see which of your own pages are being referenced by LLMs most often. If an outdated post is one of your most influential sources, LLMs are forming their perception of your brand based on old information.

Tactic 4: Finding insights from the actual LLMs answers

Peec AI stores every response to the prompts you're tracking, which means you have access to all this data for analysis. Use insights from NotebookLM to find patterns and derive insights that show you recurring complaints you can address.

Tactic 5: Monitor and participate on Reddit (carefully)

If you see Reddit threads as top influential sources in your sentiment tracking, pay attention to what's being said there. It's shaping LLM perception of your brand whether you participate or not.

Quick recap: What to do next

You now have five different ways to improve how LLMs talk about your brand:

Focus on one theme for week one. Don't try to fix everything at once.

The long-term strategy: Track sentiment → Discover real product issues → Fix those issues → Customers leave better reviews → LLMs cite improved reviews → New prospects discover you → Those customers also leave positive reviews.

Start with your weakest sentiment theme. Fix what's fixable. Check again in 30 days.