Prompt Research for GEO: Finding What Users Ask AI About Your Products

Traditional keyword research asks: "What are people typing into Google?" Prompt research asks a fundamentally different question: "What are people asking AI about my product category, and how do they ask it?" The distinction matters because AI prompts are longer, more conversational, more specific, and often part of multi-step dialogues rather than standalone searches. With 89% of B2B buyers now using AI for purchase research and 58% of consumers already replacing traditional search with AI tools, understanding prompt patterns is the foundation of any GEO content strategy.

This guide covers how to find what users ask AI about your products, the tools available for prompt mining, common query patterns, and methods for estimating prompt volume.

Why Prompt Research Differs from Keyword Research

Keyword research is built on a search-and-click model. Users type a short query, scan results, and click. The data is neat -- you get exact search volumes, competition scores, and SERP features. Tools like Google Keyword Planner, Ahrefs, and Semrush provide this data with statistical precision.

Prompt research operates in a fundamentally different paradigm. There is no centralized database of AI prompts. Users do not type three-word queries -- they write sentences or paragraphs. A single user session may involve five or ten related prompts as they refine their question through conversation. And the "results" are not links to click -- they are AI-generated responses that may or may not mention your brand.

Consider the difference in how users search for running shoes:

Google keyword: "best running shoes 2026"

AI prompt: "I've been running for about six months and I'm training for my first half marathon. I have mild overpronation and my budget is around $150. What shoes should I consider, and how do they compare on cushioning and durability?"

The AI prompt contains intent signals (training for a half marathon), physical context (overpronation), budget constraints, and evaluation criteria (cushioning and durability) that traditional keywords cannot capture. Content optimized for the keyword may not appear in responses to the prompt.

Methods for Prompt Discovery

Method 1: Direct Platform Testing

The most straightforward method is testing AI platforms directly to understand how they handle prompts in your category.

Start by asking broad category questions across ChatGPT, Perplexity, Gemini, Claude, and Copilot:

  • "What should I consider when buying [your product category]?"
  • "Recommend [your product category] for [common use case]"
  • "What's the difference between [product A] and [product B]?"

Then follow the conversation. AI search often happens in stages, with users digging deeper in the same session. Ask follow-up questions:

  • "Which of those is best for [specific constraint]?"
  • "How does [specific brand] compare?"
  • "What about durability/warranty/price?"

Document every follow-up question that feels natural. These follow-up patterns reveal the multi-step journeys your customers take.

Method 2: Customer and Support Data Mining

Your existing customer data is a goldmine for prompt research. Review:

Customer support tickets. The questions customers ask your support team often mirror the questions they ask AI. If customers frequently ask "Does your product work with [specific integration]?" they are asking AI the same question.

Product reviews. Review text reveals the language customers use and the criteria they care about. "I bought this because I needed something lightweight for travel" indicates a prompt pattern around portability.

Sales call recordings. Pre-purchase questions from sales conversations map directly to AI prompts. If prospects consistently ask about implementation time, that is a prompt category to optimize for.

Live chat transcripts. Chat interactions capture real-time questions in natural language -- the same format users bring to AI conversations.

Method 3: Community and Forum Analysis

Reddit is a leading source for both Google AI Overviews (2.2% of citations) and Perplexity (6.6% of citations). Analyzing Reddit discussions in your product category reveals the exact questions and language patterns that AI engines train on.

Search Reddit for your product category and document:

  • The questions asked most frequently
  • The specific language and terminology used
  • The comparison frameworks users employ
  • The frustrations and decision criteria they express

Do the same for Quora, industry forums, Facebook groups, and Discord communities. These platforms are where prompt patterns originate before they reach AI engines.

Method 4: AI-Powered Prompt Discovery

Use AI platforms to help you discover prompts. Ask ChatGPT or Claude:

  • "What are the most common questions people ask about [your product category]?"
  • "If someone were researching [your product type] for the first time, what would they ask?"
  • "What comparison questions come up when people evaluate [your product category]?"

While these are AI-generated approximations rather than real query data, they often surface prompt patterns that match real user behavior.

Method 5: Dedicated Prompt Research Tools

Emerging tools specifically designed for prompt discovery include:

Otterly.AI provides prompt discovery functionality that identifies which prompts trigger brand mentions and citations across AI platforms.

Frase includes AI visibility tracking with prompt-level analysis, showing which queries your content appears for across generative engines.

Profound offers prompt analysis that reveals which questions AI engines answer using your content versus competitor content.

AnswerSocrates and similar question research tools can supplement prompt research by identifying the question patterns users bring to both traditional and AI search.

Prompt research reveals consistent patterns in how users query AI about products. Understanding these patterns helps you create content that matches how AI engines encounter and process product-related questions.

Comparison Patterns

Comparison queries are among the most common product-related AI prompts. They follow predictable structures:

  • Direct comparison: "[Brand A] vs [Brand B]"
  • Multi-way comparison: "[Brand A] vs [Brand B] vs [Brand C]"
  • Attribute comparison: "Which has better [attribute], [Brand A] or [Brand B]?"
  • Use-case comparison: "For [specific use case], should I choose [Brand A] or [Brand B]?"

Comparison prompts are particularly valuable because they indicate high purchase intent. The user has narrowed their options and is making a final decision.

Recommendation Patterns

Recommendation queries ask AI to suggest products based on criteria:

  • "Best [product category] for [use case]"
  • "What [product type] should I buy if I need [specific feature]?"
  • "Recommend a [product] under [price]"
  • "What's the most [quality] [product] available?"

These queries trigger AI responses that feature product comparisons, pros-and-cons lists, and direct recommendations. Being included in these responses is the AI equivalent of ranking on page one.

Problem-Solution Patterns

Users describe problems and ask AI to suggest solutions:

  • "My [current product] keeps [problem]. What should I switch to?"
  • "How do I solve [problem] with [product category]?"
  • "I need something that [requirement]. What are my options?"

These prompts are valuable because they capture users at the moment of highest motivation to purchase.

Evaluation Patterns

Users ask AI to help them evaluate specific products:

  • "Is [brand/product] worth the price?"
  • "What are the downsides of [brand/product]?"
  • "How reliable is [brand/product] according to reviews?"

These prompts test your brand's reputation within AI knowledge bases and determine whether AI platforms recommend you with confidence or caveats.

Multi-Step Conversation Patterns

AI search often unfolds as a sequence rather than a single query. A typical product research conversation might follow this flow:

  1. Broad question: "What should I consider when buying a standing desk?"
  2. Criteria refinement: "I need one that fits in a small apartment and is under $500"
  3. Option evaluation: "How does the [Brand A] compare to the [Brand B]?"
  4. Detail drill-down: "What do users say about the [Brand A]'s build quality?"
  5. Purchase decision: "Where can I get the best price on the [Brand A]?"

Content that answers the initial query and addresses likely follow-up questions increases your chances of appearing across multiple steps in the user's conversation.

Volume Estimation for AI Prompts

Unlike traditional search where Google provides explicit search volume data, AI prompt volume is harder to estimate. However, several approaches provide reasonable approximations.

Platform Usage Data

Perplexity processed 780 million queries monthly as of May 2025. ChatGPT has over 700 million weekly users. Google AI Overviews reaches 1.5 billion monthly users. These platform-level numbers provide the total addressable market.

Category-Level Estimation

Estimate what percentage of total AI queries relate to your product category:

  1. Use traditional search volume for your category keywords as a baseline
  2. Apply the AI adoption rate for your audience demographic (58% of users have adopted AI search for product discovery)
  3. Apply a multi-prompt multiplier of two to three times (AI conversations involve multiple prompts per session)
  4. Discount by platform overlap (users may ask the same question across multiple platforms)

This gives a rough order-of-magnitude estimate. Precision is less important than understanding relative priority between different prompt categories.

Relative Volume from Tool Data

GEO tools like Ahrefs Brand Radar, which tracks 343 million-plus prompts monthly, can provide relative frequency data. While you may not get exact prompt volumes, you can determine which prompts are more common relative to others in your category.

Inference from AI Traffic Data

If your Google Analytics 4 shows referral traffic from AI platforms, the pages receiving the most AI traffic indicate which prompts are driving users to your site. High-traffic pages suggest high-volume related prompts.

Building Your Prompt Library

Compile your research into a structured prompt library organized by intent, pattern, and estimated priority:

| Prompt | Pattern | Intent | Platform Coverage | Priority | |---|---|---|---|---| | "Best [category] for [use case]" | Recommendation | High purchase | All platforms | High | | "[Brand A] vs [Brand B]" | Comparison | Decision stage | ChatGPT, Perplexity | High | | "How to choose [category]" | Informational | Research stage | All platforms | Medium | | "Is [brand] worth it?" | Evaluation | Decision stage | ChatGPT, Claude | Medium |

Update your prompt library monthly as you discover new patterns and as AI platform behavior evolves. Remove prompts where your brand consistently appears and add new prompts targeting gaps.

The Bottom Line

Prompt research is the new keyword research. It tells you what your customers are asking AI about your products, how they ask it, and where your competitors appear in the answers. Unlike keyword research, prompt data is harder to obtain and less precise, but the insights are richer because prompts capture full intent, context, and evaluation criteria. Start with direct platform testing and customer data mining, supplement with community analysis and dedicated tools, and build a prompt library that guides your content optimization. The brands that understand prompt patterns today will capture the AI-referred traffic that converts at five times the rate of traditional organic search.

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