AEO Keyword Research: Finding Prompts Not Queries

Google still dominates. But people no longer search for answers to complex problems—they ask ChatGPT instead.

Last month, 78 million people asked Claude a question. Google still sees billions of queries daily, but the type of query is fracturing. Simple questions (“best productivity app”) go to Google. Complex ones (“build a content calendar for a bootstrapped SaaS with one person doing marketing”) go to Claude or ChatGPT.

If your keyword research stops at Google Keyword Planner, you’re researching the wrong audience.

You’re not competing for rankings anymore. You’re competing for citations.

The Problem: Your SEO Keywords Don’t Work in ChatGPT

Here’s what kills most content strategies in 2026:

You spend weeks researching keywords. “Best project management software for remote teams.” You write an authoritative guide. You optimize title tags, headers, metadata. You build backlinks.

Then a prospect sits down at their laptop and types into ChatGPT:

“I manage a remote team of 8 contractors across 5 time zones. We ship features weekly. Our budget is $500/month total for tools. I need asynchronous communication, time tracking, and a simple board view. What tools should I evaluate?”

ChatGPT doesn’t return your article.

ChatGPT synthesizes answers from across the web, prioritizing sources that directly address the specific question asked. Your article on “best project management tools” is generic. The prompt ChatGPT responds to is hyper-specific. Your content doesn’t get cited because it doesn’t solve that exact problem.

This is the gap traditional keyword research misses entirely.

Why Traditional Keyword Research Fails for AEO

Keyword research tools build their data from Google search queries. Google’s algorithm rewards broad coverage and scale. A tool suggests you target “best CRM software” because thousands of people search that phrase monthly.

AEO works differently.

ChatGPT, Claude, and Gemini don’t index everything equally. They cite sources that directly answer the prompt. A guide titled “The 15 Best CRMs for Small Business” gets cited for the “best CRM” prompt. But a detailed breakdown of “How to migrate from HubSpot to Pipedrive while preserving custom fields and automation logic” gets cited for something far more specific—and something traditional keyword tools never suggest.

You can’t find these prompts in search volume data. They don’t appear in Keyword Planner. There’s no monthly search volume for “migrate HubSpot to Pipedrive while preserving custom fields.”

But there’s real demand for the answer.

Traditional keyword research optimizes for traffic volume. AEO optimizes for citation accuracy. A prompt that 50 people per month ask ChatGPT has zero search volume in Google. But if your article is the only one that answers it correctly, you’ll be cited to all 50 of them—plus to anyone who asks a similar question but phrases it differently.

The shift isn’t from quantity to quality, though that matters too. The shift is from discoverability to relevance.

How Prompts Differ from Queries

A Google query is lean. “Best productivity tools” works because Google’s algorithm can infer context.

A ChatGPT prompt is specific. It often includes constraints, preferences, and details:

Queries assume the search engine understands intent. Prompts state the intent explicitly.

This means your content needs to address specifics, not generalities. An article titled “The 5 Best Project Management Tools of 2026” will get cited maybe 20% of the time for relevant prompts. An article titled “How to Choose a Project Management Tool: Decision Framework by Team Size, Budget, and Use Case” will get cited for 80% of relevant prompts because it addresses the variables people actually ask about.

The prompt text is also longer. A Google query averages 3–5 words. A ChatGPT prompt averages 40–100 words. People are willing to spend more effort when they’re talking to a language model because they can be specific and expect relevant answers in return.

This has a direct impact on how you structure your content.

Method 1: Test Prompts in Live AI Models

This is the fastest way to understand what prompts your audience actually uses.

Go to ChatGPT, Claude, or Gemini directly. Open a new conversation. Type prompts you think your customers ask. The key is being specific, not general:

Instead of: “Tell me about email marketing tools.”

Try: “I need to send 500 personalized emails per week to contractors, and I want to trigger them based on their contract status in Airtable. Budget is $200/month. What tools integrate Airtable and allow conditional sending?”

Actually send the prompt. Read the response. Note which sources ChatGPT cites. Do this 20–30 times, varying the specifics:

The patterns that emerge show you what prompts drive citations. You’ll notice ChatGPT cites the same 3–5 sources repeatedly—those are the content pieces that answer your customer’s specific problem.

That’s your target. Write content that answers those specific prompts better than the sources currently being cited.

Method 2: Monitor Reddit and Community Spaces

Reddit is where people ask questions before they ask ChatGPT.

Go to the subreddit for your industry. Search for questions in your category. Read through 20–30 threads. Copy the exact language people use:

Pay attention to how people phrase their problems. Look for repeated constraint patterns:

These are your prompts. Copy the exact wording. These are the questions your customers will ask ChatGPT.

Discord communities, Slack groups, and community forums work the same way. The signal is strongest in places where people ask before they search. That’s where you find the real prompts.

Method 3: Conduct Customer Interviews

Ask your customers directly:

“When you’re trying to solve a problem you face in your work, what questions do you ask ChatGPT? Send me the exact prompt you use.”

Get 10–15 customers to send you their actual prompts. You’ll see patterns immediately. You’ll also spot prompts you would never have guessed.

One customer of a project management tool might ask: “I have a team that’s spread across 6 time zones. We can only overlap for 4 hours a day. How should we structure our development process and which tools enforce asynchronous communication best?”

Another asks: “We moved to a distributed model last month and our team is chaos. We used to do standup, now no one knows what anyone else is doing. What’s a practical way to set up async updates and which tools should we use?”

Same customer base. Completely different prompts.

Interview your power users, your long-term customers, and your churned customers (they often have the most interesting problems). Aim for 30+ prompts from direct customer conversations. That’s your foundation.

Building Your Prompt Library

Create a document (spreadsheet, Notion, whatever—this is working material) with three columns:

  1. Prompt: The exact question or instruction your customer asks ChatGPT
  2. Context: Who’s asking (founder, marketer, developer, etc.) and why it matters
  3. Content needs: What your company’s content should cover to get cited for this prompt

Example:

PromptContextContent Needs
I have $50K budget and need to hire a dev team. What’s the best way to structure contracts, equity, and communication with a distributed team of 6-8 people?Founder scaling from soloContract templates, equity models, async team structure, vendor comparison
We just fired our freelancer and need to migrate projects without losing history. How do I extract everything from Tool X and move it to Tool Y while keeping relationships intact?Founder problem-solvingMigration guides (step-by-step), field mapping, risk mitigation
Which CRM actually works for B2B SaaS if you’re only 2 people and one person handles everything?Early-stage founderSimple CRM guide, feature priorities for small teams, setup advice

Now you have a working library. Do this for 30–50 prompts minimum. You’ll see clusters form around specific problems.

Mapping Prompts to Content

This is where prompt research becomes a content strategy.

Take your prompt library. Group similar prompts together. For each group, ask:

  1. Do we have content that addresses this prompt?
  2. If yes, would our content get cited for this prompt specifically?
  3. If no, should we create it?

Example grouping:

Prompt Cluster: Distributed Team Setup

For this cluster, you might need:

  1. A guide: “Building Distributed Teams: Tools, Processes, and Async-First Communication”
  2. A comparison: “Async Tools for Remote Teams Compared: Slack vs. Twist vs. Loom vs. Email”
  3. A checklist: “Remote Team Launch Checklist: Week 1–4 Setup”
  4. A case study: “How a 5-Person Distributed Startup Ships Weekly”

The guide targets the core question. The comparison targets the tool-evaluation prompt. The checklist targets implementation. The case study targets the “how does anyone actually do this” prompt.

Map your existing content to clusters first. You’ll quickly see gaps.

Tools That Help (Emerging Category)

No tool owns this space yet. Here’s what exists:

ChatGPT’s “Analyze” feature can ingest web pages and tell you which prompts would cite them. Upload your article, ask “What types of questions would this page be cited for?” It’s imperfect but useful for quick feedback.

Reddit search and monitoring (native Reddit search, also Pushshift archives if they’re available) lets you find real prompts in threads. No AI tools automate this—you’ll do it manually, which is actually useful because you learn the language your customers use.

Customer interview platforms like Typeform or plain Google Forms work fine. No special AEO tools needed.

Prompt databases are emerging (some AI research groups are building index of common prompts). None are production-ready yet for commercial use.

The best tool right now is ChatGPT itself. Test your content against your prompt library manually. Iterate based on what gets cited.

The Shift from Ranking to Citation

Here’s what’s really changing:

You no longer optimize for discoverability. You optimize for relevance.

A Google ranking system asks: “How many people search for this term, and how relevant is this page?” You rank based on links, freshness, and content quality.

An AEO citation system asks: “How directly does this content answer this specific prompt?” You get cited based on how specifically you address the variables the person actually cares about.

A generic “Best CRM for Startups” ranks for thousands of search queries. It gets cited for maybe one ChatGPT prompt: “Recommend a CRM for my startup.”

A specific “How to Choose a CRM: Comparison by Team Size, Budget, Industry, and Use Case” ranks for fewer searches but gets cited for 50+ different prompts because it addresses the variables people ask about.

Traffic volume goes down. Citation frequency goes up. You’re trading reach for relevance.

This is the foundation of AEO content strategy. Find the prompts your audience asks. Create content that answers those prompts specifically. Get cited. Build authority through citation accumulation, not ranking accumulation.

Start with 30 prompts from your own customers and Reddit. That’s enough to guide your next 12 weeks of content. Scale from there.