How to Find the Prompts Your Customers Are Asking AI - And Create Content for Them

Your customers are no longer just typing three-word queries into Google. They're having full conversations with ChatGPT, Perplexity, Gemini, and Claude - asking detailed, context-rich questions that read like emails to a knowledgeable colleague. AI search queries now average 23 words compared to Google's 4-word standard , and users send 2.5 billion prompts each day on ChatGPT alone, with weekly active users reaching 900 million as of early 2026 . Here's the problem: traditional keyword research tools cannot see these prompts. They were built for a world of short search strings and keyword volume estimates. No AI platform is releasing first-party data on prompt usage, so any numbers out there are, at best, speculative . Meanwhile, visibility increasingly depends on whether a brand's content aligns with the questions people ask AI systems, not just the keywords they type into search engines . That gap between what your audience asks AI and what your content answers is where competitors are quietly winning. This guide gives you the exact methods to close it - from mining your existing data for AI-style queries to building content that AI systems want to cite.

Why Prompt Research Is the New Layer of Keyword Research

Keyword research isn't dead. But it's no longer sufficient. A new research layer - prompt research - is quickly becoming a foundational practice for SEO and generative engine optimization (GEO) . The distinction matters. Traditional keyword research analyzes search queries, ranking opportunities, and competition within the results page. Prompt research focuses on the prompts that lead AI systems to explain topics, compare options, or recommend specific tools, products, or brands . Those are two different analytical tasks that require two different workflows. Consider how the mechanics diverge. Google's AI Mode uses a "query fan-out" technique, dividing your question into subtopics and searching for each one simultaneously across multiple data sources . A single customer prompt - "What's the best CRM for a 50-person B2B sales team that integrates with Zoho?" - generates a chain of sub-queries the AI resolves independently before synthesizing an answer. Instead of mapping keyword variations alone, teams need to identify recurring prompt patterns, cluster related questions around a topic, and anticipate how a user's inquiry expands through follow-up prompts . The business case is straightforward. AI search queries average 12.3 words compared to Google's 2.8 words , and that length signals far higher specificity of intent. Analysis of 12 million website visits across 350+ businesses reveals that AI search traffic converts at 14.2% compared to Google's 2.8% . Users who arrive via an AI recommendation are already pre-qualified - they've described their exact situation to a machine that matched them with you.

Mining Google Search Console for AI-Style Queries

The most accessible source of prompt data is already connected to your domain. Your Google Search Console data - the free tool that is already connected to your domain - is quietly collecting some of these conversational queries right now . Here's why. We know it's not impossible that queries from LLM systems are available in Search Console. Data from AI Mode will be available in Search Console . In late 2024, Jason Packer wrote a report analyzing how searches from ChatGPT were actually getting leaked into Search Console reports, a story picked up by Ars Technica and confirmed by OpenAI, who claimed to have fixed the problem . Regardless of direct AI platform leakage, even if queries are just users using Google more like an LLM, it's still valuable since it reads like conversation data - an actual window into how your customers search with much longer query strings . The regex method. You can find AI queries in Google Search Console by applying a custom regex filter to your performance data. Go to Performance, select Search Results, click New filter, choose Query, select Custom regex, and paste in ^(?:\S+\s+){9,}\S+$ . This filters queries down to only those that are ten or more words long - the threshold where you start seeing searches that stop looking like keywords and start looking like prompts . Once applied, scan the results. You are going to see queries that read nothing like search terms. They will look like questions typed into a chat window - specific, contextual, and full of detail. Some queries will mention your brand directly. Some will mention competitors. Some will be broad category questions from buyers still figuring out what solution they need . Refining with intent filters. For greater precision, use a regex to find queries starting with interrogatives like "why," "how," or "what" and combine it with the word-count filter. By combining them, you get a single, powerful filter that finds queries that are both conversational and complex . Export the results, then prioritize. High-priority candidates are those with high impression volume and low click-through rates - indicating questions where people see your site but don't find a satisfying answer.

Expanding Your Prompt List With People Also Ask and Community Data

Search Console shows where you already appear. But you also need to discover questions you're not showing up for - the gaps that represent unmet demand.

People Also Ask as a Prompt Proxy

AI search engines often break a single user prompt down into multiple sub-queries to gather a complete answer. Google's PAA boxes are essentially a "fan-out" of the primary search intent. By expanding your list with these questions, you aren't just getting more keywords - you are expanding your coverage of the potential sub-queries an AI will use to build its overview . The workflow: take your filtered GSC queries, run each one through Google, and extract the PAA questions that appear. You can easily turn an initial list of 100 keywords into over 400 highly relevant questions in minutes - and repeat this process 2-3 times to go beyond 1,000 prompts . Tools like Ahrefs Toolbar can automate PAA extraction at scale, and Ahrefs Brand Radar provides insights into over 240 million prompts across popular AI platforms .

Reddit, Quora, and Forums

If people are actively discussing a problem in forums, chances are they'll ask AI about it too. By mining these discussions, you can build a list of AI prompts that reflect genuine user intent . This isn't theoretical. Every Reddit thread about your brand is shaping today's search results as well as training tomorrow's AI answers. Systems like Google AI Overviews, ChatGPT, and Claude incorporate Reddit content into their core knowledge bases - meaning a comment posted today can influence recommendations buyers see months from now . Focus on threads where people ask for recommendations, compare products, or describe specific frustrations. Reddit tends to show up in prompts shaped by a specific persona or scenario - usually framed as questions that buyers ask to reduce risk . Capture the exact language used. These aren't keywords. They're full sentences that mirror how people talk to AI tools.

Internal Sources Most Teams Overlook

Your support tickets and sales calls are prompt goldmines. Export your support tickets and analyze them for recurring questions, repeated phrasing patterns, and common customer pain points. Review sales call transcripts and extract the most common "Can you…," "How does…," and similar questions that reflect real purchase objections . Support teams often discover that the most frequent questions aren't what marketing assumed, which means there's a notable gap you need to bridge .

Clustering Prompts Into Content-Ready Groups

A raw list of 500+ prompts is noise without structure. The next step is grouping those prompts by semantic intent so you can plan content efficiently.

Prompts are naturally long-winded, and people often ask the same question in dozens of different ways . "What's the best tool to check AI Overview rankings?" and "How can I monitor my visibility in AI-generated summaries?" carry identical intent. That's why you don't need to track every single variation . Cluster by SERP similarity. Group the keywords by SERP similarity - if two prompts produce overlapping search results, they represent the same underlying topic. Tools like thruuu's clustering feature or Semrush's keyword grouping can automate this. Organize by funnel stage. Prompt clusters can be defined by intent, the marketing funnel stage they belong to, the products and services you offer, or anything else that makes sense for your business . A practical framework:

• Awareness prompts: "What is [concept]?" and "How does [technology] work?"
• Consideration prompts: "Best [product category] for [specific use case]" and "[Product A] vs [Product B]"
• Decision prompts: "[Brand name] pricing" and "Is [brand] worth it for [specific need]?"

As a general rule, 30 well-chosen, strategically important prompts tracked daily will always outperform 200 random prompts tracked weekly . Depth on the right clusters beats breadth across random queries every time.

Creating Content That AI Systems Actually Cite

Knowing the prompts is only half the equation. You need to create content structured so AI platforms want to pull from it.

Write for Extraction, Not Just Ranking

In practice, AI-citable content means clear, direct answers to specific questions, self-contained explanations, fact-based comparisons, and concise definitions that make sense without surrounding context. AI systems tend to pull individual passages, not entire pages, so structure and clarity matter more than length . Each H2 should answer one cluster of prompts directly. Lead each section with a concise, quotable answer (40-60 words), then expand with evidence, nuance, and examples. Think of every subheading as a standalone unit an AI might extract.

Format for AI Retrieval Patterns

Listicles, articles, and product pages are the most common citations in AI Mode, ChatGPT, and Perplexity. LLMs cite different content types for different intents - 45.48% of informational queries cite articles, while 40.86% of commercial queries cite listicles . Structure your content accordingly:

• Informational prompts: Explainer articles with clear definitions, step-by-step processes, and supporting data
• Comparison prompts: Tables, side-by-side evaluations, and pros/cons lists
• Recommendation prompts: Ranked listicles with specific criteria and qualifying context

Source citation improves by 30% when schema markup is included . Add FAQ schema for question-answer pairs drawn directly from your prompt clusters. 78% of AI Overview responses feature either ordered or unordered lists - incorporate structured lists wherever the content warrants them.

Prioritize Freshness and Authority

AI platforms tend to cite content that is 25.7% "fresher" than content cited in traditional search . Update your key pages quarterly. Add publication and last-updated dates. Reference current data. Content freshness has become a critical ranking factor for AI search visibility, and in competitive spaces, recency often serves as a tiebreaker between similarly authoritative sources . Authority doesn't only mean domain authority in the traditional sense. Only 12% of URLs cited by ChatGPT, Perplexity, and Copilot rank in Google's top 10 search results, and 80% of LLM citations don't even rank in Google's top 100 for the original query . AI platforms evaluate authority through different signals: consistent topical coverage, mentions across the web, expert bylines, and original data. A focused site with deep coverage of a narrow topic can outcompete major publishers on specific prompts.

Tracking Your AI Visibility Over Time

You've identified prompts and created content. Now you need to measure whether AI systems are actually citing you.

Manual Spot-Checking

The simplest approach: Run your priority queries through tools like Perplexity, ChatGPT, and Google Search to see which domains are cited as sources in the AI-generated answers . Document whether your brand appears, what it's cited for, and which competitors show up instead. Do this weekly for your top 30 prompts. Be aware of volatility. Tests on the same query showed that AI Mode had overlapping results just 9.2% of the time . Single snapshots lie. Group related prompts to get a directional, aggregate overview of response commonalities, rather than prioritizing any individual result .

Dedicated AI Visibility Tools

For teams scaling beyond manual checks, several platforms have emerged. The best tools for this workflow in 2026 are Conductor, Peec, OtterlyAI, Akii, and Profound, because they support prompt libraries, tracking across major AI engines, and reporting you can operationalize . Ahrefs' Brand Radar monitors brand visibility across 243M+ monthly prompts - and those prompts are derived from real "People Also Ask" data, not synthetic queries . Semrush's Enterprise AIO, Writesonic's GEO tool, and seoClarity's ArcAI offer similar capabilities with different strengths. The key requirement: any tool you choose should let you input your own prompt list, track across multiple AI platforms, and report trends over time.

Server Log Analysis

A complementary approach that doesn't require paid tools: You can dive into server logs to see when ChatGPT-User requests pages on your site. This is not a bot that continually crawls the web; instead, it's used when users ask ChatGPT a question and that page may help in generating a response . Look for user agents from Perplexity-User, DuckAssistBot, and MistralAI-User alongside ChatGPT's agent. The pages they crawl reveal which content AI systems consider citation-worthy - and which content they ignore.

Acting on Content Gaps: Where AI Cites Reddit Instead of You

One of the most actionable signals in prompt research is discovering where AI cites community content instead of your brand. This reliance on community forums signals a massive content gap - AI models primarily default to community forums when authoritative brand publishers fail to address real-world, decision-stage questions . When you run your priority prompts through ChatGPT or Perplexity and see Reddit threads cited as sources, that's a direct content brief. AI systems don't just match topics - they match intent. High-level questions pull from authoritative publishers. Decision-stage queries lean toward comparisons and evaluations. Practical or edge-case questions surface in forums and community content . The response playbook has two tracks: Track 1: Create the content AI is missing. Read the Reddit threads being cited. Identify the specific questions, the nuances discussed, and the experience-based insights that earned the citation. Then create content on your site that addresses those same questions with equal authenticity but greater authority - including original data, expert perspective, and structured formatting AI can extract. Track 2: Participate in the communities. When a community resource like Reddit is cited, contribute a high-quality answer and include your content as helpful context. Follow community rules . This isn't about link-dropping. It's about being a recognized voice in the conversations AI already references. It's like the new version of link-building - except now, you're building AI citations .

Building a Weekly Prompt Research Workflow

Abstract strategy means nothing without a repeatable process. Here's a weekly cadence that takes roughly four hours per week once set up: Monday (1 hour): Pull your GSC regex export. Scan for new long-tail queries that appeared in the past 7 days. Flag any with rising impressions but low CTR. Tuesday (1 hour): Run your top 30 priority prompts through ChatGPT and Perplexity. Note which brands are cited, what content format appears, and whether your visibility changed from last week. Wednesday (30 minutes): Scan relevant subreddits and Quora topics for new questions. Add any recurring themes to your prompt tracking list. Thursday (1 hour): Update or create one piece of content based on the week's biggest gap - a prompt cluster where you have no content or where competitors are being cited instead. Friday (30 minutes): Log all changes in a shared tracker. Note which prompt clusters improved, which declined, and which need new content next week. This isn't a campaign with an end date. GEO isn't a one-time project - it's an ongoing practice. The AI landscape changes constantly, and your content needs to keep pace . --- The shift from keywords to prompts isn't a theoretical trend happening on conference stages. It's already showing up in your Search Console data, in your referral logs, and in the questions your sales team hears every week. In 2026, successful websites must track both traditional keywords and AI-driven prompts to stay competitive . The practitioners who thrive won't be the ones who panic about AI replacing search. They'll be the ones who treat prompt research as what it is: the natural evolution of keyword research for a world where your customers expect answers, not just links. Start with your Search Console data this week. Filter for the long queries. Read them carefully. Those sentences aren't just data points - they're your customers telling you exactly what they need, in their own words.