ChatGPT handles 2.5 billion prompts every day. And the visitors who land on your site from those conversations? They convert at 15.9%, roughly 9x the rate of Google organic. The brands showing up in those answers aren't just getting eyeballs. They're getting revenue.
Here's the problem: most AEO advice dead-ends at "optimize your content for AI search." Nobody tells you which AEO prompts your brand should actually go after, or how to pick them.
That's what we're covering here. By the end you'll have six concrete ways to uncover the right prompts and a workable system for deciding which ones deserve your attention first.
Why AEO Prompts Are Fundamentally Different from Keywords
Keywords are static. Universal, even. Type "project management software" into Google and everyone more or less sees the same results page. AI engines don't play by those rules.
ChatGPT, Perplexity, and their peers have memory. They know your industry, the tools you've brought up before, the problems you keep circling back to. Two people can ask the identical question and get wildly different answers because the engine tailors its response to everything it already knows about each person. A founder running a 10-person startup and a VP of Engineering at a 2,000-person enterprise both ask "what's the best project management tool?" and the recommendations they get back won't overlap much.
So AEO prompt targeting is a moving target in a way that keywords never were. You're not optimizing for a query. You're optimizing for a query filtered through someone's identity, history, and context. The practical takeaway: cast a wide net across the prompts that matter to your audience. One underlying question can show up in dozens of personalized variations.
Understanding Query Fanout: What Happens When an AEO Prompt Is Submitted
Before you can pick the right prompts, you need to understand the mechanics of what happens after someone hits enter.
The engine almost never treats a prompt as a single query. Instead, it runs query fanout, splitting the original prompt into several sub-queries and searching for each one in parallel. Those sub-query results get stitched together into the final answer. And here's the thing: the individual sub-queries look far more like old-school search queries than the original prompt ever did.
Take a prompt like "What's the best way for a SaaS company to reduce churn in the first 90 days?" That might fan out into "SaaS churn benchmarks," "onboarding best practices for SaaS," "customer success in first 90 days," and "reducing early churn strategies." Each sub-query pulls content on its own. Your content has to hold up at that sub-query level, not just match the top-level prompt.
This is probably the single most useful thing to internalize about AEO: the prompts people type are the front door, but fanout queries are where the actual ranking happens. When you're building a page around a high-priority AEO prompt, think through the sub-queries it's likely to spawn, and make sure you're answering each of them well. Lumen surfaces these query fanouts for your target AEO prompts, so you can see exactly which sub-queries the engines are firing off behind the scenes.
Six Methods for Discovering the Right AEO Prompts
Start wide. Aim for 100–200 candidate prompts before you start cutting. Each method below works at a different stage of the funnel and produces a different flavor of insight. Mix and match.
1. Top-down product, persona, use-case, and funnel-stage mapping
This is your cold-start method, best when you're spinning up an AEO prompt strategy from scratch with no historical data. Build a four-axis matrix:
- Products/services × Target personas × Use cases and pain points × Funnel stage
At every intersection, write 3–5 prompts that persona would realistically ask an AI engine at that stage. A founder in awareness mode asks completely different things than a procurement manager at the decision stage, even when the product is the same. Mapping by funnel stage keeps you honest about covering the full journey:
- Awareness: "What is X?" or "Why do companies use X?" or "What are the main approaches to solving Y?"
- Consideration: "Best X for [specific situation]?" or "X vs. Y for [use case]?"
- Decision: "Is [Brand] worth it?" or "How does [Brand] compare to [Competitor]?"
Think of this matrix as scaffolding. As real data rolls in from Methods 2–4, it'll sharpen and eventually replace your guesses. But for a blank-slate start, it keeps your prompt list from accidentally skipping whole segments of your audience.
2. Community forums
Reddit and Quora are gold mines here. They hold the raw, unpolished questions real people ask before pulling the trigger on a purchase, phrased in exactly the natural language that ends up in AI prompts.
Go search your core topics on Reddit. Read the threads. Notice how people frame things: the phrasing, the specific constraints they drop in, the head-to-head comparisons they want. Those are your prompts, basically handed to you.
Why are forums better than keyword tools for prompt discovery? Because the questions aren't squeezed into search-bar shorthand. They sound the way people actually talk. Which is the same way they talk to ChatGPT.
3. Sales call transcript analysis
Your sales reps hear the same handful of questions over and over. "How does this integrate with HubSpot?" "How does your pricing stack up against [Competitor]?" "What does implementation actually look like?"
Those aren't just objections to handle on a call. They're prompts waiting to be targeted. The same questions that come up on discovery calls are almost certainly being asked to ChatGPT by buyers who never reach out to your team.
The manual approach: pull your last 20 call recordings, flag the recurring questions, and cluster by theme. One important detail: pay attention to how the prospect phrases the question, not how your rep answers it. That phrasing is what maps to how people actually word their prompts.
4. Google Search Console
Your GSC performance report is sitting on thousands of questions people already ask Google, and many of them are structurally identical to the sub-queries AI engines generate during fanout. The catch? They're buried deep in the query report.
Filter for natural-language patterns: questions that start with "how," "what," "which," "best," or "should I." These line up directly with the prompt structures that engines break down and search against.
5. Internal team interviews
Customer success, sales, support. These teams field the same questions week after week. Those questions are prompts.
Set up dedicated prompt mining sessions. Ask your CS managers for the top 10 support ticket questions from the last 90 days. Get sales reps to list their most common discovery-call objections. Have product teams write down the use cases customers ask about most. What you get back is phrased in your customers' own words, covering niche scenarios and edge cases that generic content never touches. And niche prompts? Those are often the easiest to win.
6. Traditional SEO keyword data
If you've already got a mature SEO program (here's how SEO and AEO differ), your long-tail keyword data is a decent starting point. Filter for queries with "how," "what," "which," "best X for," and "difference between." Those question-shaped structures tend to mirror how people prompt AI engines.
One catch, though: keyword volume tells you how often people search Google. It says nothing about how often they ask ChatGPT or Perplexity. A query with 50 monthly searches might generate far more prompts than its volume would suggest, or far fewer. Use keyword data to spot topics and question shapes.
Maintaining AEO Prompt Visibility: A Program, Not a Campaign
You can't just set this up and walk away. The brands cited for a given prompt today might not be the ones cited next month. Engines are constantly reshuffling as new content gets published, models get updated, and user patterns shift. Your prompt strategy needs to be a living, breathing program.
The number-one killer of visibility? Stale content. Every time a competitor drops a fresher, better-structured piece on a prompt you're targeting, your odds of getting cited go down. Run a 60–90 day refresh cycle on every page tied to a high-value prompt.
Lumen's dashboard tracks AEO prompt performance across every major answer engine automatically: which prompts you're winning, which you're losing, and what shifted since last cycle. So you know exactly where to spend your next refresh.
Conclusion
AEO prompt discovery is not a one-and-done project. The questions buyers ask AI engines change as your market moves, competitors ship new content, and models retrain. Brands that consistently win in AI search treat AEO prompt selection as an ongoing discipline, not a box to check at launch.
Start broad. Use the six methods to build a big candidate list, then zero in on the AEO prompts where you have real credibility and a clear content opening. Depth beats breadth. One genuinely excellent piece will outperform ten shallow ones every time. Revisit your prompt list quarterly. Refresh your top-performing content every 60–90 days.
Book a call with us to learn how Lumen can help you win AI search.