How to Set Up AI Prompt Tracking for AI Search Visibility

AI search is reshaping how people discover, compare, and choose products and solutions. Instead of typing short keywords, users ask full questions—and answer engines respond with synthesized, citation-driven recommendations. That means your visibility no longer depends on ranking well on SERPs. It depends on whether an AI model chooses you as the answer.

But the insights you get from AI visibility monitoring are only as strong as the way you set up your tracking. The prompts you track determine what you learn about your brand presence, your competitors, and the topics where you’re winning or losing.

Set it up right, and you’ll see where AI is recommending you, where it’s choosing competitors, and where content gaps are holding you back. Set it up wrong, and the data won’t reflect the real user journey. Even worse, it will limit your ability to control overall brand sentiment and make it impossible to get an accurate picture of your brand’s AI visibility.

That’s why AI prompt tracking matters. You’re not just tracking keywords anymore—you’re tracking full conversations, intents, and citations across answer engines.

Topics vs. prompts: What’s the difference?

Before you choose an AI search prompt tracking approach, it’s important to understand how AEO platforms organize AI visibility data. Most tools don’t track individual prompts one by one—that wouldn’t scale for enterprise teams, and it wouldn’t reflect how people actually search. Instead, they use a two-layer structure:

Topics = the categories you want to measure

Topics are broad, high-level themes tied to your solutions, service areas, or core content pillars.

Examples include:

• Cloud security platforms
• Customer data management

A topic represents a full area of your offering. Think of it as the “container” for closely related questions (AKA prompts) your ideal buyers might ask during research and evaluation.

Prompts = the specific questions buyers ask AI engines

Prompts are the conversational queries answer engines respond to—comparisons, recommendations, pricing questions, implementation guidance, and strategic evaluations.

Most AEO platforms automatically generate dozens or even hundreds of prompts per topic based on:

• Intent (comparison, pricing, recommendation, integration, best practices)
• Persona (e.g., head of IT, SEO lead, enterprise marketer, CISO)
• Branded vs. unbranded variations

Because prompts are generated in bulk, teams rarely add or edit individual prompts manually. When you update a topic, you’re really updating an entire set of prompts, which can reshape your tracking insights significantly.

Why this distinction matters

• Built for scale: Topic-level tracking lets enterprise teams manage AI visibility without maintaining prompts individually.
• Aligned to real buyer behavior: Topics reflect how B2B users research, compare, and evaluate solutions across the funnel.
• Focused on revenue-driving categories: A structured hierarchy keeps tracking tied to the areas that influence pipeline and authority.
• Strategic by design: Since one topic can generate many prompts, updates should be intentional and strategic to maintain a stable reporting baseline.

Understanding this structure gives you the foundation for choosing the right prompt tracking approach and building a dataset that mirrors how real B2B buyers evaluate solutions in AI-driven search experiences.

AI prompt tracking: Where do you start?

AI search prompt tracking isn’t about monitoring as many queries as possible—it’s about choosing the right approach to reflect how your customers actually search in AI experiences. Most brands use one of two strategies: starting broad to understand their category, or digging into data to reverse-engineer where answer engines are already paying attention.

Here’s how these approaches work and where they differ.

Approach #1: Start with the topics that matter

If you’re new to prompt tracking (or want an approach that’s simple, scalable, and easy to maintain), begin by defining the broad topics you care about. This is essentially a top-down approach, where you establish the high-level themes that represent your brand, then let prompts become more granular from there.

A topic-led strategy means selecting categories at the same altitude: Think “Handbags” instead of “Black leather crossbody bag for travel,” or “Answer engine optimization” instead of “AEO vs. GEO.”

Topics should align with your core content pillars or product lines so your tracking reflects the parts of the business you want AI visibility for.

From there, you build depth using structured inputs:

• Personas: Broad audience types that shape how prompts are phrased—such as IT decision-maker evaluating software, marketing leader researching analytics tools, or consumer comparing home security options.
• Intents: Select multiple intents—comparison, recommendation, pricing, informational—to understand where you win or lose different stages of the customer journey.
• Mix of branded/unbranded: Aim for around 75% unbranded/25% branded.
  • Why fewer branded topics and prompts? You likely dominate your own brand queries already, so unbranded prompts reveal where you’re truly competitive and where you’re missing from the conversation.

This approach gives you a clean category-level baseline that mirrors the top of the user journey and keeps your tracking consistent across topics.

Approach #2: Start from the data you already have

More advanced teams (or those with access to technical resources) often start by looking at how answer engines already interact with their website. This aligns with a bottom-up approach, where real performance signals guide what you track.

Here, you reverse-engineer your prompt strategy by identifying pages that get attention from AI models, even if they don’t appear in AI answers yet.

A data-led workflow usually looks like this:

• Check log files or server activity: Look for bot hits—particularly from AI-related user agents—to see which pages models are crawling. Some engines frequently read pages that never get cited.
• Check analytics for AI referral behavior: This traffic can be grouped under direct or social, so be sure to look across channel/source groups for accurate insights. If you spot patterns on pages AI models frequently visit, it’s a sign they consider the content relevant.
• Take action when there’s a page-level mismatch: If Page A (e.g., a sofa product page) gets AI/LLM bot hits but little to no AI referral traffic, the page is being read but not cited. That’s your signal to begin tracking prompts related to that page or topic immediately.

A bottom-up approach is especially useful for surfacing overlooked opportunities, diagnosing underperformance, and identifying where your content might be relevant but not authoritative enough for AI models to recommend.

The strongest AEO platforms translate these signals into page-level visibility, showing which URLs answer engines trust—and which ones they consistently read but ignore.

Which approach should you choose?

Both approaches work—but they solve different problems, and the strongest brands intentionally use both. A topic-led strategy gives you a broad, directional understanding of how AI models see your product, service, or business category. It reveals where you’re strong, where competitors overtake you, and which themes you need to reinforce with content.

A data-led strategy, on the other hand, tells you where to act right now. It surfaces disconnects between what AI models read and what they choose to cite, highlights weak spots in authority or coverage, and uncovers opportunities you’d never find by brainstorming topics alone. It’s the difference between knowing the landscape and knowing the exact roads that need fixing.

When you combine the two, you get a feedback loop:

• Category-level tracking shows you whether you’re owning the conversations that matter.
• Data-led tracking shows you what’s preventing you from owning them.
• And together, they give you a complete AI visibility view to inform your content roadmap, optimization priorities, and resource planning.

The real goal is to build a tracking system that mirrors how people actually engage in AI search—moving fluidly between broad exploration and specific, intent-driven questions. When your prompts reflect that natural journey, your visibility data becomes a true leading indicator of how AI models understand your brand today—and how they’ll represent you tomorrow.

Best practices for AI prompt tracking

Once you’ve chosen where to start, the next step is making sure your tracking setup produces clean, reliable insights. Good AI search prompt tracking isn’t about volume; it’s about aligning what you track with your brand, your content strategy, and how your customers actually use AI search.

These best practices will help you build a system that’s both consistent and strategically meaningful.

Track only the topics that matter most to your strategy

The topics you track should align closely with your product lines, service offerings, and/or content pillars. With most AEO platforms limiting how many topics and prompts you can track overall, being selective isn’t optional—it’s a crucial part of setting up AI prompt tracking that produces accurate, actionable insights.

One of the biggest mistakes brands make is treating topic and prompt tracking like keywordKeyword
A keyword is what users write into a search engine when they want to find something specific.
Learn more tracking: going too broad, too granular, or too disconnected from what they actually create content about. Your topics should sit at the same altitude and reflect the areas where visibility truly impacts your business.

When you choose categories that align with the content you publish (or plan to publish), you’re setting up AI models to understand your authority where it matters—not diluting your footprint across unrelated prompts.

A good rule of thumb: If you wouldn’t invest content resources into a topic, don’t track it.

Lean more heavily on unbranded prompts

In traditional search, brands could bid on competitor terms to appear ahead of them in paid results. That isn’t possible in AI search—there’s no pay-to-play option. Every answer is organic, which makes the balance between branded and unbranded prompts especially important.

Branded prompts do help you understand how answer engines talk about your company, but they often paint an overly positive picture. Most brands naturally perform well for their own name, so relying too heavily on branded prompts inflates your perceived visibility.

You should still track some branded prompts, particularly because AI engines often surface comparison content, and you need visibility into how you’re represented there. But this should be a smaller slice of your overall mix.

A good benchmark is to keep branded prompts to 25% or less of your total prompts, with the remaining majority focused on unbranded categories. The point isn’t to confirm you own your brand terms. It’s to see whether AI recommends you when users aren’t already thinking about you. The goal is to increase discoverability and awareness of your brand.

Track a manageable number of topics and prompts

AEO platforms give you some flexibility in how many topics and prompts you can track, but more isn’t always better, even within platform limits. Enterprise teams get the strongest insights when they focus on the categories that directly support pipeline, competitive positioning, and content investment.

Topics:

Most organizations track 10–30 topics, each tied to a core solution area or content pillar. This keeps visibility aligned to the parts of the business where AI recommendations truly matter. Too many topics dilute your dataset; too few make it hard to see gaps.

Prompts per topic:

Across the industry, platforms typically support dozens to hundreds of prompts per topic, driven by your chosen intents and personas. A common range is:

• Minimum: ~5
• Typical: 50–150
• Upper limit: ~300–500

This gives you enough depth to understand how buyers explore a category—evaluations, comparisons, integrations, pricing, troubleshooting, and more.

Total prompt volume:

Even large teams usually operate within a few thousand prompts total. This offers broad coverage without overwhelming reporting or exceeding usage limits.

Why it matters:

Because a single topic can generate dozens or hundreds of prompts, every new topic has a multiplier effect. A focused set of topics with deep, intentional prompt coverage leads to cleaner data, more stable trend lines, and clearer insights into where AI engines trust—or overlook—your brand.

Surface page-level insights to understand what AI engines trust

The most valuable AEO platforms don’t just show whether your brand appears—they show which pages are driving that visibility. Page-level insights reveal:

• Which URLs answer engines consistently cite
• Which pages they read but don’t recommend
• Where competitors are earning trust
• Where your content lacks depth, clarity, or authority

This level of granularity helps teams connect AI visibility directly to on-page improvements, identify gaps across solution areas, and prioritize content enhancements that move the needle.

​​Update prompts on a consistent, cadence-based schedule

Any time you add or change topics or prompts, your tracking results shift. That’s why updates should follow a defined cadence—ideally tied to your content planning cycles, such as annual, quarterly, or biannual reviews—rather than happening spontaneously.

Ad hoc changes lead to volatility that makes visibility trend reporting difficult, especially when sharing results with leadership. A consistent cadence ensures your insights remain accurate, stable, and aligned with content or product planning cycles.

It also encourages intentionality. New topics or prompt groups should be added because they support a strategic initiative, not because they simply seem interesting today.

Choose the right AEO / GEO engines for the right visibility insights

Not all answer engines behave the same way. Each one provides a different lens into your visibility, and choosing intentionally helps you avoid unnecessary noise.

Here’s a high-level guide on what each major answer engine or AI-generated search resultSearch Result
Search results refer to the list created by search engines in response to a query.
Learn more is best suited for:

• ChatGPT (Auto): Closest to real user behavior, since it blends internal knowledge with browsing. Ideal for baseline AI visibility.
• ChatGPT (Search): Forces web-grounded answers. Use it when you need a precise view of sourcing and competitive positioning.
• Google AI Overview: One of the most prominent and highly visible features in Google’s search experience. Critical for understanding how your brand appears in AI-generated summaries, where billions of users still begin their research.
• Perplexity: Prioritizes citations. Helpful for understanding how and why your content is referenced and sourced.
• Gemini and other emerging engines: Track when relevant to your audience or vertical. These engines can offer additional perspective on niche markets or emerging AI search behaviors.

Think of answer engines as complementary perspectives. You don’t need to track across all of them for every topic, but choosing the right mix gives you a more complete picture of your overall AI presence.

How to implement AI prompt tracking

Once you’ve defined your strategy and best practices, the final step is putting prompt tracking into action. While every AEO / GEO platform handles setup differently, the core principles are the same:

Define your topics and brand associations. Choose the topics that matter most to your business and tie them to the right brands or product lines. These should align with your core offerings or content pillars, so the prompts you generate later map cleanly to what you actually want visibility for.

Select the answer engines you want to monitor. Pick the engines that make sense for your audience and goals. Some engines give you a realistic picture of everyday search behavior, while others emphasize citations or provide visibility into high-volume AI experiences. Aim for a thoughtful mix rather than tracking everything by default.

Set your tracking locations and frequency. Choose the markets that matter most to your brand. Most teams start with country-level tracking, and weekly cadence remains the industry standard—frequent enough to surface trends without overwhelming your performance data.

Configure your prompts with purpose. This is where your strategic inputs take shape. Most platforms rely on three main elements:

• Prompt type (branded, unbranded, or both)
• Intent (comparison, recommendation, pricing, informational, etc.)
• Personas (broad audience perspectives that influence phrasing)

Be intentional. Remove low-value intents where needed and make sure personas reflect real customer behavior, not internal assumptions.

Review and refine prompts before launch. Take time to read through the prompts you’ll be tracking. Adjust phrasing where necessary, remove outliers, and confirm everything aligns with how your customers actually explore the topic. A quick quality check goes a long way in keeping your dataset clean.

Check your resource usage. Before activating tracking, look at how many credits or runs your setup will consume. Different engines often have different “exchange rates,” so make sure your engine mix aligns with your budget and long-term goals.

Launch and monitor early signals. Once tracking is live, let it run for a few cycles before making changes. Early results are useful for spotting gaps, unexpected competitors, or prompts that need refinement. After the initial adjustment period, you’ll have a steady baseline to support ongoing analysis.

Use page-level insights. As results begin to populate, pay close attention to page-level visibility. Seeing which URLs answer engines cite—and which they ignore—gives you the clearest signals on where to optimize, consolidate, or expand content.

FAQs

AI search visibility is still new for many teams, which means you’ll likely get questions—from leadership, cross-functional partners, and others—as you start tracking AI prompts. Here are answers to the questions that come up most often.

Why don’t prompts have “search volume” the way keywords do?

Prompts represent full conversations, not short phrases, so users phrase them in countless ways. Instead of chasing volume, the best AI prompt tracking functionality focuses on intent—understanding how answer engines respond to the types of questions real people ask.

Why track engines that don’t drive a lot of traffic yet?

While ChatGPT currently accounts for more than 87% of AI referral traffic, that doesn’t mean you should overlook emerging engines. AI-generated search results like Google’s AI Overviews and answer engines like Perplexity offer early signals on which sources are being cited and how your content is interpreted across different environments. They’re not the core of your strategy, but they add valuable context.

My branded visibility looks strong. Does that mean our AI presence is good?

Not necessarily. Most brands perform well for their own name, so branded prompts can inflate your perceived visibility. The real battleground is unbranded prompts—the questions users ask before they’ve chosen a brand. A strong AI presence means showing up in category-level conversations, not just brand-specific ones.

How many prompts should I track per topic?

You want enough prompts to reflect the full customer journey, but not so many that your tracking becomes cluttered, costly, or difficult to interpret. The goal isn’t volume for volume’s sake; it’s a meaningful spread of prompts that map to real buyer questions.

Most platforms require a minimum number per topic, but the focus should always be on quality over volume. Prompts should be broad enough to cover diverse intents but tightly aligned to your strategy.

Within Conductor, prompts are automatically generated based on the topics you input, and most enterprise teams track a minimum of around 100 prompts per topic. This level of depth is what allows you to capture true AI visibility at scale without manually building prompts yourself.

How often should you update your prompts or topics?

Use a predictable cadence, such as quarterly or biannually. Updating too often introduces volatility and resets your visibility baseline, making long-term trend analysis much harder. Add new prompts only when they align with strategic content or product initiatives.

What does it mean if an engine reads our page but doesn’t cite it?

It usually means your content is relevant but not authoritative enough to earn a mention. Track prompts related to that topic, compare your page to cited competitors, and look for opportunities to optimize for clarity, depth, or trust signals.

Why does our AI visibility differ from our SEO rankings?

Answer engines don’t rank links—they generate answers. That means they lean heavily on publishers, aggregators, forums, and brands with strong topical authorityTopical Authority
Topical authority is the expertise and credibility a website demonstrates on a subject through comprehensive, interconnected, high-quality content.
Learn more, even if those sources don’t rank highly in traditional SERPs. AI visibility reflects who the model trusts, not who holds the top organic position.

How long does it take to see meaningful AI visibility trends?

You’ll start seeing signals within the first few cycles, but meaningful patterns typically appear after several weeks of consistent tracking. Because AI prompts represent conversations rather than fixed keywords, trends stabilize over time as you gather more data across engines.

What if we don’t appear in AI answers at all yet?

That’s more common than you think. Most brands are just beginning to invest in AI visibility. Use early tracking to identify which competitors or publishers consistently appear, what content they offer that you don’t, and where the biggest gaps exist. This becomes your roadmap for building authority in answer engines.

Can I optimize directly for AI engines?

You can’t optimize for the engines, but you can optimize for the behavior that the engines reward: high-quality content, clear explanations, strong topical authority, structured dataStructured Data
Structured data is the term used to describe schema markup on websites. With the help of this code, search engines can understand the content of URLs more easily, resulting in enhanced results in the search engine results page known as rich results. Typical examples of this are ratings, events and much more. The Conductor glossary below contains everything you need to know about structured data.
Learn more, and reliable sourcing. AI visibility is an outcome of how well your content answers real user questions, not something you can manipulate directly.

Setting yourself up for AI visibility success

AI search isn’t a trend to react to—it’s a shift in how people find information and make decisions. As answer engines become the default interface for discovery, the brands that win won’t be the ones obsessing over rankingsRankings
Rankings in SEO refers to a website’s position in the search engine results page.
Learn more. They’ll be the ones who understand how these models think: what they cite, what they ignore, and what they need in order to consider a brand authoritative.

AI prompt tracking gives you that visibility. It exposes the real conversations happening across AI systems so you can build content that earns trust where decisions are actually being made.

Brands that learn to interpret these signals will set the pace in AI search, not follow it.