AI search has changed how people discover brands, products, and solutions online.

Instead of typing short keywords into Google, users now ask conversational questions in tools like ChatGPT, Perplexity, Gemini, and Claude. These systems don’t just rank pages, they generate recommendations.

That shift has created an entirely new challenge for marketers: prompt research. But most advice around prompt research today is flawed.

Many articles recommend generating hundreds of synthetic prompts with AI, tracking internal fan-out queries, or creating unrealistic “persona prompts” no real customer would ever type. The result is noisy reporting, inflated datasets, and visibility tracking disconnected from actual buying behavior.

Prompt research is not about collecting as many prompts as possible.

It’s about understanding the real human journeys that influence AI recommendations.

In this guide, we’ll show you how to do prompt research properly, using representative prompts, real search behavior, and commercially meaningful user intent.

What Is Prompt Research?

Prompt research is the process of identifying the questions and conversations people use in AI search tools when researching products, services, brands, or problems.

Examples include:

• “What’s the best CRM for a small sales team?”

• “Which AI SEO tools are worth it?”

• “How do I improve visibility in ChatGPT?”

• “HubSpot vs Salesforce for B2B SaaS”

• “How do I integrate Stripe with Webflow?”

These prompts influence:

• which brands get mentioned

• which websites get cited

• which products get recommended

• which companies become associated with a category

Prompt research helps marketers understand where and how their brand appears in AI-generated answers.

Why Prompt Research Is Different From Keyword Research

Traditional SEO focused heavily on exact-match keywords.

AI search works differently.

Large language models interpret:

• intent

• entities

• context

• semantics

• conversational history

This means users can ask the same question in dozens of different ways while still triggering similar retrieval behavior.

For example:

• “Best CRM for startups”

• “What CRM should a small SaaS company use?”

• “Which CRM is easiest for a startup sales team?”

These prompts may retrieve similar brands and sources despite having different wording.

This is why prompt research should focus on:

• buying journeys

• topical intent

• representative prompts

• retrieval environments

…. not exact-match phrasing.

Most Prompt Research Advice Is Wrong

A lot of current AI SEO content still treats prompts like traditional keywords.

That creates several major problems.

Problem #1: Tracking Hundreds Of AI-Generated Prompts

Many tools and articles recommend generating massive prompt lists with AI.

This is usually a mistake.

AI-generated prompts are often:

• unrealistic

• overly specific

• repetitive

• disconnected from real customer behavior

For example, AI systems frequently generate prompts like:

“As a B2B SaaS marketing manager at a mid-sized company, what’s the best AI visibility platform for enterprise GEO tracking?”

Real users rarely search like this.

Most people ask much simpler questions.

Using synthetic prompts at scale creates reporting noise instead of strategic insight.

AI can help brainstorm ideas, but it should never become your primary source of prompt research.

Problem #2: Tracking Fan-Out Queries (instead of optimizing for them)

Some GEO guides recommend tracking “fan-out queries”, the internal searches an LLM may generate during retrieval.

This fundamentally misunderstands how AI search works.

Fan-out queries are retrieval mechanics, not user intent.

The user never searched those terms.

The original human prompt is what matters strategically.

Different AI systems generate different retrieval expansions internally, and these change constantly. Tracking them creates unstable and misleading datasets.

Optimizing content for broader retrieval relevance makes sense.

Tracking machine-generated retrieval variations as “market prompts” does not.

Keep in mind, optimizing for query fan-outs is still a powerful way to get mentions and/or citations.

Fan-out queries represent the additional searches an AI system performs internally to gather context before generating an answer. While they shouldn’t be tracked as primary prompts because users never see or enter them, they can reveal the topics, entities, and sources AI models rely on during retrieval. Analyzing these fan-outs can help identify content gaps and strengthen your topical coverage, ultimately increasing the likelihood that your content is retrieved and cited for the original human prompt.

Problem #3: Obsessing Over Prompt Length

You’ll often see claims like:

• “AI prompts average 42 words”

• “Users search differently in ChatGPT”

The problem is that most of these statistics have no reliable methodology behind them.

Prompt behavior changes constantly based on:

• device type

• voice mode

• memory

• follow-up chats

• autocomplete

• interface design

• personalization

The exact length of prompts matters far less than:

• intent

• context

• commercial relevance

• retrieval alignment

Problem #4: Ignoring Post-Purchase Prompts

Most prompt research frameworks end once a customer has made a purchasing decision. In reality, AI search plays an equally important role after the sale. 

Customers increasingly rely on tools like ChatGPT to learn a product, troubleshoot issues, discover advanced features, compare integrations, or find best practices. 

These post-purchase conversations influence customer success, retention, expansion opportunities, and even future recommendations. If your brand isn’t visible when existing customers ask for help, you’re missing a significant part of the AI customer journey. 

Prompt research should therefore extend beyond acquisition and include the questions users ask throughout the entire lifecycle of using your product or service.

The Goal Is Not More Prompts

The goal is better prompts.

Most companies do not need to track hundreds or thousands of prompts.

In reality, a carefully selected set of 25–30 representative prompts can often provide a highly accurate picture of AI visibility within a market.

We call this a representative prompt set.

A strong prompt set should:

• reflect real customer journeys

• cover major buying stages

• represent high-commercial-intent searches

• include category discovery prompts

• include post-purchase engagement prompts

The key is selecting prompts that actually influence recommendations and purchasing decisions.

The 5 Stages Of AI Search Prompt Research

Most prompt research frameworks stop at purchase intent.

That misses a huge part of modern AI search behavior.

Users continue relying on AI systems long after becoming customers.

That means prompt research should cover five stages, not four.

1. Awareness

Users are discovering a problem or learning about a category.

Examples:

• “What is AI search optimization?”

• “How do brands appear in ChatGPT?”

• “Why are fewer people clicking Google results?”

These prompts shape category perception.

2. Consideration

Users begin exploring potential solution types.

Examples:

• “Best AI SEO tools”

• “How do companies track AI visibility?”

• “Top GEO platforms for agencies”

At this stage, brands start entering recommendation sets. 

3. Decision

Users validate final purchasing concerns.

Examples:

• “Is Genrank worth it?”

• “Which AI SEO platform has the best reporting?”

• “What’s the easiest GEO tool to implement?”

These prompts often influence conversion. Sometimes users also compare vendors directly.

Examples:

• “Genrank vs Profound”

• “Best AI visibility platform for enterprises”

• “Which GEO tool has citation tracking?”

These prompts carry strong commercial intent.

4. Engagement & Customer Success

This is the stage most frameworks completely ignore.

After becoming customers, users continue using AI tools for:

• onboarding

• troubleshooting

• implementation

• integrations

• support

• optimization

Examples:

• “How do I set up prompt tracking in Genrank?”

• “How do I improve citation coverage?”

• “Best practices for AI visibility reporting”

These prompts matter because they:

• reinforce brand familiarity

• influence retention

• shape future recommendations

• generate long-term authority signals

In AI search, the customer journey does not end after purchase.

The Best Prompt Research Method: Query Mining

The best source of prompts is usually real search behavior.

One of the most effective techniques is mining:

• Google Search Console

• Bing Webmaster Tools

for long-tail conversational queries.

A simple but powerful workflow is:

1. Export your search queries

2. Filter for queries with 5+ words

3. Cluster them by intent

4. Identify recurring customer problems

This often reveals highly valuable prompts because these searches already represent:

• real users

• real language

• real intent

• real demand

You can use regex filtering to isolate conversational-style searches quickly.

For example:

• queries containing question words

• long-tail searches

• comparison searches

• problem-oriented searches

This approach is significantly more reliable than generating prompts artificially with AI.

People Also Ask 

Another valuable source of prompt ideas is Google’s People Also Ask (PAA) data. These questions represent real queries from users and often mirror the informational and exploratory prompts people ask AI assistants. 

Tools such as Genrank, Ahrefs, Semrush, and other SEO platforms give you access to PAA questions that you can search by topic or keyword. Rather than copying every question, look for recurring themes, comparisons, objections, and follow-up questions. 

These can help you identify prompt clusters and uncover gaps in your current tracking. Combined with Search Console data, PAA databases provide a reliable, human-generated foundation for building a representative prompt set.

Prompt Clusters Matter More Than Exact Prompts

AI search is highly dynamic.

The exact wording of a prompt may change from one user to another while still producing similar retrieval behavior.

That’s why prompt clusters are more important than exact-match prompts.

Instead of tracking:

• one isolated prompt

Track:

• a cluster of prompts around the same intent

For example:

AI Visibility Platform Cluster

• “Best AI SEO tools”

• “Top GEO platforms”

• “How do companies track ChatGPT visibility?”

• “Best AI search analytics software”

This gives you a much more stable view of:

• brand visibility

• recommendation frequency

• citation patterns

• retrieval environments

Focus On Commercially Meaningful Prompts

Not all prompts are equally valuable.

One of the biggest mistakes companies make is prioritizing curiosity traffic over buying-intent prompts.

For example:

Low Commercial Value

• “What is GEO?”

• “How does AI search work?”

High Commercial Value

• “Best GEO tools for agencies”

• “Which AI visibility platform should I use?”

Informational prompts still matter for category awareness, but prompt research should prioritize prompts that influence:

• recommendations

• vendor selection

• purchasing decisions

Otherwise, teams end up optimizing visibility for prompts that never generate revenue.

Prompt Research Is Continuous

Prompt research is not a one-time setup task.

AI search behavior evolves rapidly because:

• models change

• retrieval systems evolve

• interfaces shift

• user behavior adapts

• AI systems personalize responses differently over time

Your representative prompt set should evolve continuously based on:

• market changes

• customer language

• product positioning

• emerging categories

• competitor movements

The companies that win AI search visibility will not be the ones tracking the most prompts.

They will be the ones tracking the right prompts.

Final Thoughts

Most prompt research advice today overcomplicates the problem.

You do not need:

• thousands of synthetic prompts

• AI-generated prompt dumps

• machine-generated fan-out tracking

• unrealistic persona phrasing

You need:

• representative prompts

• real customer language

• commercially meaningful intent

• strong prompt clustering

• continuous refinement

The goal of prompt research is not simply to monitor prompts.

It’s to understand:

• when AI systems recommend your brand

• why certain sources get retrieved

• which journeys influence purchasing decisions

• how your company becomes associated with a category

That’s what actually drives visibility in AI search.