What Prompts Should You Track for AI Search?

The prompts you track are the foundation of your AI Search visibility strategy. Get them wrong, and you'll spend months monitoring queries that don't matter. Get them right, and you'll know exactly where your brand shows up, where it doesn't, and what to fix.

Most teams start tracking AI visibility by picking a handful of branded queries and a few generic industry terms. That's a start, but it misses the prompts that actually drive discovery, consideration, and conversion in AI search engines like ChatGPT, Gemini, Perplexity, and Copilot.

This guide breaks down how to build a prompt tracking list that covers every stage of the buyer journey, how to prioritize prompts by business impact, and how to avoid the most common mistakes teams make when setting up AI search monitoring. In short, most companies should begin by tracking around 50 natural-language prompts covering awareness, consideration, comparison, and decision stages, then expand their prompt portfolio as they add new products, markets, competitors, and customer segments.

Why Does Prompt Selection Matter for AI Search Monitoring?

In traditional SEO, you track keywords. In AI search, you track prompts, and the difference is more than semantic.

A keyword like "best CRM software" maps to a single search results page. But when someone types that same phrase into ChatGPT or Perplexity, the AI engine doesn't just match keywords. It interprets intent, breaks the query into sub-questions (a process called query fan-out), and synthesizes answers from dozens of sources. One prompt can trigger 5-15 query fan-outs, each pulling from different content.

This means the prompts you track need to be more intentional than a traditional keyword list. You're not just monitoring rankings. You're monitoring whether AI engines understand your brand well enough to recommend it when users ask questions in natural language.

Don’t Track SEO Keywords, Track Real Conversations

One of the fastest ways to get misleading AI visibility data is to copy your SEO keyword list directly into an AI visibility platform.

For example, a US agency recently onboarded one of their clients into Superlines and initially tracked prompts such as:

• CRM software
• GEO software
• AI visibility platform

The results looked surprisingly poor on ChatGPT and Claude, even though the brand had strong visibility in Google AI Mode and AI Overviews.

The reason became obvious after reviewing the tracked prompts.

Unlike traditional search engines, AI assistants are rarely used with keyword fragments. People ask complete questions and describe what they’re trying to achieve.

Instead of tracking:

• CRM software
• GEO platform
• AI search visibility

track prompts such as:

• What is the best CRM for SaaS startups?
• Which GEO platform should marketing agencies use?
• What are the best AI search visibility tools?

Once the agency converted their keyword list into conversational prompts, the client’s visibility immediately reflected what real users were actually seeing.

A good rule of thumb is simple:

Use your SEO keywords to identify topics, but always convert them into natural language prompts before tracking them.

The brands tracking the right conversations today are the ones building long-term AI Search visibility.

What Types of Prompts Should You Track?

The goal isn’t to invent prompts. It’s to mirror the conversations your ideal customers naturally have with AI assistants throughout their buying journey. Not all prompts are created equal. A strong tracking list covers four distinct intent categories, each serving a different purpose in your visibility strategy.

Awareness Prompts: How People Discover Your Category

These are the broadest queries where users are learning about a topic, not yet looking for specific solutions. Examples:

• "What is [your category]?"
• "How does [your product type] work?"
• "Why do companies need [your solution area]?"
• "What are the benefits of [your approach]?"

Awareness prompts matter because they're where AI engines form their initial understanding of which brands belong in a category. If your brand never appears in "What is generative engine optimization?" responses, AI engines may not associate you with GEO at all, even for more specific queries later.

Consideration Prompts: How People Evaluate Options

These prompts signal active research. Users know what they need and are exploring solutions:

• "Best [product type] for [use case]"
• "Top [product type] tools in 2026"
• "How to choose a [product type]"
• "[Product type] for small businesses / enterprises / agencies"

Consideration prompts are where most AI visibility battles are won or lost.

Comparison Prompts: How People Narrow Their Shortlist

Comparison prompts are high-intent queries where users are deciding between specific options:

• "[Your brand] vs [competitor]"
• "Best [competitor name] alternatives"
• "[Your brand] pricing"
• "[Your brand] reviews"
• "Is [your brand] better than [competitor] for [use case]?"

These prompts have the highest conversion potential. When someone asks ChatGPT "Superlines vs Profound," they're close to a purchase decision. If your brand isn't mentioned (or is mentioned unfavorably), you lose the deal before your sales team even knows about it.

Decision Prompts: How People Validate Their Choice

Decision prompts are the final check before purchase:

• "Is [your brand] worth it?"
• "[Your brand] pros and cons"
• "[Your brand] case studies"
• "How to get started with [your brand]"

These prompts are often overlooked in tracking lists, but they're critical. A user who's already leaning toward your product might change their mind if the AI engine surfaces negative information or recommends a competitor at this stage.

These four prompt categories map naturally to the traditional marketing funnel: Awareness belongs to TOFU (Top of the funnel), Consideration and Comparison belong to MOFU (Middle of the funnel) , and Decision belongs to BOFU (Bottom of the funnel).

Recommended Prompt Distribution

Once you’ve identified the different prompt types, the next question becomes how much attention each category deserves.

Across millions of prompts analyzed through Superlines, we’ve consistently observed that middle-of-the-funnel (MOFU) prompts are the most sensitive to wording variations, meaning small changes in how a question is asked can influence which brands AI engines recommend, while top-of-the-funnel (TOFU) and bottom-of-the-funnel (BOFU) prompts generally produce much more consistent responses because the underlying user intent is clearer.

As a starting point, we recommend allocating your tracking roughly like this:

• 25% Top-of-Funnel (TOFU): Educational and category discovery prompts
• 50% Middle-of-Funnel (MOFU): “Best”, comparison, alternative and evaluation prompts
• 25% Bottom-of-Funnel (BOFU): Brand-specific, pricing, reviews and decision-stage prompts

Why?

Top-of-the-funnel prompts usually focus on understanding a category or concept. Different phrasings often lead AI engines to very similar answers because they’re trying to answer the same underlying question.

Bottom-of-the-funnel prompts are typically brand-specific, such as “Superlines pricing” or “Superlines reviews”, where the user’s intent is already well defined.

Middle-of-the-funnel prompts behave differently.

This is where users compare vendors, evaluate alternatives, and explore different approaches. Small wording differences can significantly influence which brands AI engines recommend because the intent is broader and there are often many possible solutions.

Because of this, dedicating roughly half of your tracked prompts to MOFU queries usually provides the clearest picture of how discoverable your brand is to new potential customers across AI Search.

Do I Need to Track Every Prompt Variation?

Fortunately, no.

One of the biggest misconceptions about AI search monitoring is that you need to think of every possible way someone might phrase a question.

In our recent research, we found that more than 90% of prompt variations express essentially the same underlying intent. For example:

• “Best CRM for startups”
• “Which CRM should a startup use?”
• “What’s the best CRM platform for a small SaaS company?”

Although the wording differs, they’re asking AI engines to solve the same problem. As long as the underlying intent remains the same, AI engines often retrieve very similar information.

That’s why we always recommend focusing on tracking buyer intent, rather than trying to build hundreds of nearly identical prompt variations.

The one exception is the middle of the funnel.

Because MOFU prompts are more sensitive to wording differences, this is where prompt variations have the greatest impact on which brands AI engines recommend. That’s exactly why MOFU prompts deserve the largest share of your tracking.

The good news is that you don’t have to manage those variations manually.

Superlines automatically creates and analyzes prompt variations behind the scenes, allowing you to focus on tracking the right buying intent while still capturing how real users naturally phrase their questions across different AI platforms.

How Many Prompts Should You Track?

The right number depends on your product complexity and competitive landscape, but here's a practical framework:

For most companies, 50 prompts is a good starting point. This provides enough coverage across the buyer journey without becoming difficult to manage. Break it down roughly as:

• 10-15 awareness prompts covering your core category and adjacent topics
• 12-20 consideration prompts targeting "best," "top," and "how to choose" queries
• 5-10 comparison prompts covering your brand vs. key competitors and alternatives queries
• 3-5 decision prompts covering pricing, reviews, and validation queries

Scaling up: 100-200 prompts. Once you've established baseline visibility, expand into:

• Long-tail variations of your top-performing prompts
• Industry-specific and vertical-specific queries
• Geographic variations (e.g., "best CRM for European companies")
• Platform-specific queries (e.g., queries that perform differently on ChatGPT vs. Gemini vs. Perplexity)

Enterprise scale: 500+ prompts. Organizations operating across multiple products, markets, languages, and customer segments often need hundreds of tracked prompts. It’s also important to track prompts separately by country and language so you can compare visibility accurately across different markets.

The goal isn’t to track more prompts. It’s to track the right prompts. A well-designed prompt portfolio covers every stage of the buyer journey, your most important competitors, the markets you operate in, and the AI platforms your customers actually use.

Keep Branded and Non-Branded Prompts Separate

One of the most common mistakes we see is tracking branded and non-branded prompts inside the same project.

For example:

Branded

• Superlines pricing
• Superlines reviews
• Superlines alternatives

Non-branded

• Best GEO software
• Best AI visibility platform
• Best answer engine optimization tools

The problem is that branded prompts almost always have much higher visibility because the user already knows your company.

If you mix both together, your dashboard starts showing artificially high visibility numbers that don’t accurately reflect your ability to get discovered by new buyers.

We recommend creating separate tracking projects:

• Non-branded prompts (discovery)
• Branded prompts (brand monitoring)

This gives you two completely different but equally valuable datasets.

Your non-branded prompts show how often AI engines recommend you to people who have never heard of your company.

Your branded prompts show how your brand is presented when people are already evaluating you.

Keeping them separate makes your visibility metrics significantly more meaningful.

Why Query Fan-Out Data Is One of the Biggest AI Search Advantages

Unlike traditional search engines, AI assistants don’t simply answer the exact prompt a user enters.

Instead, they break the prompt into multiple underlying retrieval queries, commonly referred to as query fan-outs. These are the searches the AI performs behind the scenes to gather information before generating its final answer.

For example, if someone asks:

“What is the best CRM for startups?”

the AI might internally search for topics such as:

• CRM pricing
• CRM integrations
• CRM comparison

Depending on the AI engine, a single prompt may trigger anywhere from 5 to 15 different query fan-outs.

Most AI visibility platforms never expose this information because they rely solely on APIs.

Because Superlines collects data directly from the AI search interfaces, it can capture these underlying query fan-outs and show you which ones are consistently being used for every tracked prompt.

Over time, clear patterns begin to emerge.

These patterns become valuable optimization opportunities because they reveal the concepts AI engines actually retrieve when answering a particular topic.

For example, if “CRM integrations” consistently appears as one of the most common fan-outs behind a prompt you’re tracking, you can strengthen your content by naturally incorporating that concept into:

• URL structure
• H1, H2 and H3 headings
• body content
• supporting sections

Instead of guessing what AI engines are looking for, you’re optimizing for the exact retrieval concepts they already use behind the scenes. Think of query fan-outs as the retrieval language AI engines use internally. Understanding that language gives you a significant advantage when optimizing content for AI search.

You can learn more tips about how to improve content visibility in AI search.

How to Build Your Initial Prompt List

Here's a step-by-step process for building a prompt tracking list from scratch.

Step 1: Start With Your Existing Keyword Data

Your SEO keyword list is a useful starting point, but it needs translation. Convert keywords into natural language prompts:

The shift is from keyword fragments to complete questions. AI search users type (or speak) in full sentences, and the prompts you track should reflect that.

Step 2: Mine Your Sales and Support Conversations

Your sales team hears the exact questions prospects ask before buying. Your support team hears the questions customers ask after buying. Both are goldmines for prompt ideas:

• Sales calls: "How does your tool compare to [competitor]?" "Can your platform do [specific thing]?" "What's your pricing for [team size]?"
• Support tickets: "How do I set up [feature]?" "Does your tool integrate with [platform]?" "What's the best way to [accomplish goal]?"
• Chat logs: Look for recurring questions in your website chat, demo requests, and onboarding conversations.

These real-world questions map directly to the prompts people type into AI search engines.

Step 3: Analyze Competitor Visibility

Check which prompts your competitors appear in and you don't. This is where competitive gap analysis becomes critical.

For each major competitor, track:

• "[Competitor] vs [your brand]"
• "Best [competitor] alternatives"
• "[Competitor] pricing"
• Prompts where the competitor is cited but you're absent

The prompts where competitors consistently appear and you don't are your highest-priority gaps. These represent active demand that's flowing to competitors because AI engines don't associate your brand with those queries.

Step 4: Track the Same Prompts Across Multiple AI Engines

The prompt itself shouldn’t change between AI platforms.

Instead, track the same prompt across ChatGPT, Gemini, Perplexity, Claude, Copilot, and other AI engines to understand how each platform recommends brands differently.

Although the user intent stays the same, each AI engine has its own retrieval system, model behavior, and source preferences. The same prompt may recommend completely different companies depending on where it’s asked.

By monitoring the same prompt across multiple AI engines, you gain a much clearer picture of your overall AI visibility instead of optimizing for a single platform.

What Are the Most Common Prompt Tracking Mistakes?

Tracking Only Branded Queries

If your prompt list is 80% "[your brand] + keyword," you're only measuring visibility among people who already know you. The bigger opportunity is in unbranded discovery prompts where new customers find your category for the first time.

A healthy prompt list should be at least 60-70% unbranded queries.

Tracking Too Few Prompts

Teams that track 5-10 prompts get a dangerously incomplete picture. AI search visibility varies dramatically across prompt phrasings, platforms, and time. A small sample size leads to false confidence ("We're visible!") or false alarm ("We disappeared!") when the reality is more nuanced.

Never Updating the List

AI search behavior changes fast. New phrasing patterns emerge as platforms add features (like Google's AI Mode, which launched in 2025). Competitor content changes what gets cited. Industry events create new query clusters.

Review and update your prompt list at least monthly. Add prompts for new competitors, new product features, and emerging industry topics. Remove prompts that are no longer relevant or have zero search volume.

Treating All Platforms the Same

A prompt that returns your brand on Perplexity might completely ignore you on ChatGPT. Each AI platform has different training data, different citation preferences, and different response formats. Track your prompts across all major platforms, not just one.

How Often Should You Review and Update Your Prompt List?

The short answer: review it every three months at minimum.

AI Search evolves much faster than traditional search. New competitors emerge, products launch, industries change, and AI engines continuously update how they retrieve and recommend information.

Your prompt list shouldn’t be something you create once and forget. It should evolve alongside your business.

We recommend reviewing your prompt portfolio regularly and adding prompts whenever:

• You launch new products or features.
• You start targeting new industries or customer segments.
• New trends or use cases emerge in your category.

At the same time, remove prompts that:

• Are no longer relevant to your business.
• No longer align with your target audience.
• Duplicate other prompts without providing additional insight.

Every quarter, perform a broader audit by comparing your tracked prompts against your current positioning, sales conversations, customer research, and competitive landscape. This helps ensure you’re still measuring the conversations that matter most to your business.

The brands that treat prompt selection as an ongoing process, rather than a one-time setup, tend to build much stronger long-term AI visibility. For a deeper look at evaluating your overall AI search presence, see our GEO audit framework.

How to Know You’re Tracking the Right Prompts

A good prompt portfolio should give you a realistic view of how your brand is discovered throughout the buying journey.

If you’ve followed the framework in this guide, your tracked prompts should naturally cover educational, consideration, comparison, and decision-stage conversations instead of concentrating on just one part of the funnel.

For example, you may discover that some prompts consistently have a 0% Branded Response Rate (BRR), meaning AI engines rarely recommend any brands for those conversations. While you may choose to keep some of these prompts as long-term opportunities, they often contribute less insight than prompts where AI engines actively recommend products and services.

As your dataset grows, you should also be able to answer questions such as:

• Which stages of the buyer journey generate the highest brand visibility?
• Which competitor comparisons are becoming more competitive?
• Which prompts consistently recommend brands, and which rarely surface any companies at all?
• Which conversations offer the biggest opportunity to increase your share of voice?

Ultimately, the goal isn’t to build the largest prompt library. It’s to track the conversations that influence buying decisions and give yourself reliable data to improve your visibility over time.

As your AI visibility improves, you should also expect broader business signals to strengthen over time, including higher brand awareness, more branded searches, increased demo requests, and a healthier sales pipeline. Because AI increasingly answers questions without sending clicks, share of voice has become a much more meaningful long-term success metric than referral traffic alone.

The key metrics for measuring generative search success include brand visibility rate, citation rate, and share of voice. Your prompt list should give you clear data on all three.

Start Tracking the Prompts That Actually Matter

The difference between brands that win in AI Search and those that don't often comes down to what they measure. Tracking the wrong prompts gives you a false sense of security. Tracking the right ones gives you a roadmap for exactly where to focus your content, your competitive strategy, and your optimization efforts.

Start with around 50 prompts across all four intent stages. Review your prompt portfolio at least every quarter and expand it as your products, markets, competitors, and customer conversations evolve. Make sure you’re covering every AI platform where your audience searches, not just ChatGPT.

Superlines tracks brand visibility across 10+ AI platforms using real UI scraping, so the data reflects what users actually see, not API approximations. Beyond raw tracking, Superlines surfaces opportunity-based insights that show you which actions to prioritize, where competitors are winning, and what content to create or update next. Its MCP server also lets AI agents query your visibility data directly, so prompt tracking can feed into fully agentic content workflows.

Start a free Superlines trial to see which prompts your brand appears in today, and which ones you're missing!