How do I plan AEO/GEO Ready content from real user prompts?
Plan AI SEO ready content from real user prompts by collecting 50-100 actual questions from ChatGPT, Perplexity, and Gemini, clustering them by intent, and mapping one URL per distinct prompt. Question-centric content planning ensures you’re answering questions users actually ask rather than questions you think they ask. Hardik Shah, Digital Growth Strategist and AI-Native Consulting Leader, specializes in AI-driven search optimization and AEO strategy for enterprise clients across industries. “This is priority methodology in our governance framework,” Shah explains. “Starting with prompt research instead of keyword research fundamentally changes content effectiveness because you’re matching how users actually formulate questions to AI systems.”
What is question-centric content planning?
Question-centric content planning starts with collecting real user prompts from AI systems, analyzing them to understand true user intent, and creating content that directly answers those specific questions using the same language users employ.
This approach inverts traditional content planning that started with keyword research and search volume.
Simple explanation
Instead of guessing what people might search for, you actually watch what questions they ask ChatGPT and Perplexity. Then you write content answering those exact questions using the same words they used.
Technical explanation
Question-centric planning aligns content creation with natural language query patterns that dominate AI search behavior. By collecting prompts across multiple LLM platforms, you identify the conversational phrasing, specificity level, and intent patterns that characterize how users actually seek information. This prompt corpus then drives heading structure, content depth, and semantic focus, maximizing semantic matching during RAG retrieval.
Practical example
Traditional keyword-based planning:
Keyword research shows “solar panel cost” gets 10,000 monthly searches. You create comprehensive guide covering costs, financing, ROI, maintenance, incentives in one 3,000-word page.
Question-centric planning:
Prompt research reveals users ask seven distinct questions:
- “How much does solar panel installation cost?”
- “What financing options exist for solar panels?”
- “How do I calculate solar panel ROI?”
- “What tax incentives are available for solar?”
- “Do solar panels require maintenance?”
- “How long does solar installation take?”
- “What size solar system do I need?”
You create seven focused pages, each answering one question using the exact phrasing from prompts.
How do you collect 50-100 real prompts?
Systematic prompt collection across multiple AI platforms and prompt variations.
Collection process:
- Identify your topic area: What subject does your business cover?
- Open multiple AI platforms:
- ChatGPT (https://chat.openai.com)
- Perplexity (https://www.perplexity.ai)
- Google Gemini (https://gemini.google.com)
- Claude (https://claude.ai)
- Ask starter questions: Begin with broad questions in your topic area
- Note follow-up suggestions: Each AI suggests related questions – capture these
- Vary question phrasing: Ask the same thing different ways to see phrasing variations
- Explore different expertise levels: Ask beginner questions, intermediate questions, expert questions
- Document everything: Copy exact question phrasing into a spreadsheet
Example prompt collection session (solar panels):
Ask ChatGPT: “What should I know about solar panels?”
Note the response, then capture follow-up questions suggested: “How much do solar panels cost?” “How long do they last?” “What maintenance is required?”
Ask Perplexity same question, capture their phrasing of follow-ups.
Ask Gemini, capture their variations.
Continue with more specific questions: “How do I calculate if solar panels are worth it?” “What’s the payback period for residential solar?” “How many solar panels do I need for my house?”
After 30-45 minutes across platforms, you’ll have 50-100 distinct questions.
How do you cluster prompts by intent?
Group prompts that seek the same underlying information even if phrased differently.
Clustering process:
- Dump all prompts into spreadsheet
- Identify core intent: What is the user actually trying to learn?
- Group similar intents: Multiple phrasings of the same question go together
- Name each cluster: Use the most common or clearest phrasing
- Identify primary prompt: Within each cluster, which phrasing appears most often?
Example clustering:
Cluster: Solar Installation Cost
- “How much does solar panel installation cost?”
- “What’s the average price for residential solar?”
- “How much should I budget for solar panels?”
- “What do solar panels cost to install?”
- “Solar panel installation cost for typical home?”
All five prompts seek the same information (cost), so they cluster together. The first phrasing might be most common, becoming your primary target.
Cluster: Solar ROI Calculation
- “How do I calculate solar panel ROI?”
- “What’s the payback period for solar?”
- “How long until solar panels pay for themselves?”
- “Are solar panels worth the investment?”
These cluster together around investment return calculation.
What makes a prompt distinct enough for separate content?
Two prompts need separate content when they seek different information or require different depth of answer.
Same prompt cluster (one page):
- “How long do solar panels last?”
- “What’s the lifespan of solar panels?”
- “How many years do solar panels work?”
These are lexical variations seeking identical information.
Different prompt clusters (separate pages):
- “How long do solar panels last?” (lifespan question)
- “What maintenance do solar panels need?” (maintenance question)
- “How much does solar panel maintenance cost?” (cost question)
Each requires different content depth and focus.
Decision criteria:
- Would someone satisfied with answer to prompt A still ask prompt B?
- Does answering prompt A require substantially different information than prompt B?
- Are the prompts at different specificity levels (overview vs. deep dive)?
If yes to any of these, create separate content.
How do you map one URL per prompt?
After clustering, assign each cluster its own dedicated URL using primary prompt phrasing.
URL mapping example:
| Prompt Cluster | Primary Phrasing | Assigned URL |
|---|---|---|
| Solar cost | “How much does solar panel installation cost?” | /how-much-solar-installation-cost |
| Solar lifespan | “How long do solar panels last?” | /how-long-solar-panels-last |
| Solar ROI | “How do I calculate solar panel ROI?” | /calculate-solar-panel-roi |
| Solar size | “What size solar system do I need?” | /what-size-solar-system-needed |
Each URL targets one primary prompt cluster. The URL slug mirrors the question structure.
URL structure decisions:
Some teams use question-format URLs (/how-much-does-solar-cost)
Others use keyword-format URLs (/solar-installation-cost)
Testing suggests question-format URLs align slightly better with conversational queries, but the difference is modest. Consistency matters more than format choice.
What happens to keyword research in this methodology?
Keyword research becomes secondary validation rather than primary driver.
New workflow:
- Start with prompt research (50-100 real questions)
- Cluster prompts by intent
- Check keyword volume for top clusters (validates demand)
- Prioritize content creation based on: (business value) + (demand volume) + (competitive gap)
- Create content answering prompts directly
Traditional workflow:
- Start with keyword research (high-volume terms)
- Create content targeting those keywords
- Optimize for search algorithms
- Hope content matches what users actually want
The question-centric approach front-loads user intent understanding. Keywords validate that enough people care about the question to justify content investment.
How many prompts should become content?
Not all 100 prompts need immediate content. Prioritize based on business value and feasibility.
Prioritization framework:
Tier 1 (Create first):
- High business value (consideration-stage questions)
- Prompts where you have unique expertise
- Questions with clear answers you can provide
- Topics where current content is weak or missing
Tier 2 (Create within 90 days):
- Moderate business value
- Supporting questions that help users understand Tier 1 topics
- Questions with moderate competition
Tier 3 (Create when capacity allows):
- Lower business value but users still ask
- Very competitive topics where differentiation is hard
- Edge case questions with small audiences
Don’t create content for:
- Prompts seeking information you don’t have
- Questions outside your expertise area
- Topics requiring expertise you can’t credibly claim
ScaleGrowth.Digital, an AI-native consulting firm serving enterprise clients across industries, typically recommends clients target 20-30 priority prompts in the first implementation phase. “You don’t need to answer every question users ask. Answer the questions where your expertise creates genuine value.”
Should content structure mirror prompt phrasing exactly?
Yes. Use the prompt as your H1, with related prompt variations as H2s.
Content structure from prompts:
H1: How much does solar panel installation cost?
H2: What affects solar installation pricing?
[Answer from prompt cluster about cost factors]
H2: How much do solar panels cost per watt?
[Answer from prompt cluster about pricing units]
H2: What’s the average cost for residential solar?
[Answer from prompt cluster about typical pricing]
Each H2 comes from a related prompt in the same cluster. This ensures you’re answering not just the primary question but related questions users ask immediately after.
How do you handle prompts at different expertise levels?
Create different content for beginner, intermediate, and expert audiences if the same topic needs different treatment.
Example: Same topic, different expertise
Beginner prompt: “What is AI search optimization?”
Content: Basic definition, why it matters, simple examples
URL: /what-is-ai-search-optimization
Intermediate prompt: “How do I implement AI search optimization?”
Content: Implementation steps, tools needed, timeline
URL: /implement-ai-search-optimization
Expert prompt: “What RAG architecture considerations affect AI citation probability?”
Content: Technical deep dive into retrieval-augmented generation
URL: /rag-architecture-ai-citations
These three prompts are related but seek different depth. Trying to answer all three in one piece creates the vector dilution problem.
What tools help with prompt collection?
Manual collection works but tools can accelerate the process.
Prompt collection approaches:
Manual (free):
- Open multiple AI chat interfaces
- Document questions and suggestions in spreadsheet
- Time investment: 2-3 hours for 50-100 prompts
- Advantage: Direct understanding of user phrasing
Semi-automated:
- Use AI to generate question variations
- Ask ChatGPT: “What are 50 questions people ask about [topic]?”
- Validate against what real users ask
- Time investment: 30-60 minutes
- Advantage: Faster, but requires validation
Answer engine monitoring:
- Track what questions trigger AI responses about your topics
- Use tools like those mentioned in AI search monitoring
- Ongoing process rather than one-time research
- Advantage: Captures actual user behavior over time
Shah recommends starting manual: “The first time you do prompt research, do it manually. You’ll develop intuition for how users phrase questions differently than you expect. After you’ve done this a few times, automation makes sense.”
How often should prompt research update?
Continuous collection with quarterly content planning updates.
Prompt research cadence:
Initial phase: Deep research collecting 50-100 prompts, cluster analysis, content planning
Ongoing: Continuous light collection (note interesting prompts when you encounter them)
Quarterly review: Analyze new prompts collected, identify emerging question patterns, adjust content roadmap
Annual deep dive: Comprehensive prompt research refresh, validate that content still matches current phrasing patterns
User language evolves. Questions shift as your industry changes. Prompt research isn’t one-time activity.
What about prompts your business can’t answer?
Identify gaps where users ask questions outside your expertise. This reveals partnership or content expansion opportunities.
Gap analysis:
Prompts cluster into three categories:
- Questions you can answer authoritatively (your current expertise)
- Questions you could answer with research (adjacent expertise)
- Questions outside your domain (other expertise required)
Category 1: Create content
Category 2: Decide whether to build expertise or partner
Category 3: Recognize these aren’t your content opportunities
Example: If you’re a solar installation company and prompt research reveals many questions about electrical permitting regulations, that might be outside your expertise. Don’t create weak content on topics where you lack authority. Either build that expertise or acknowledge it’s not your content focus.
How does prompt-based planning change content briefs?
Content briefs become answer requirements rather than keyword targets.
Traditional content brief:
- Target keyword: “solar panel cost”
- Secondary keywords: solar installation price, solar system cost
- Word count: 2,500
- Competitors to beat: [URLs]
Prompt-based content brief:
- Primary prompt: “How much does solar panel installation cost?”
- Related prompts: “What affects solar pricing?” “How much per watt?” “What’s typical for 2,000 sq ft home?”
- Answer requirements: Cost range, factors affecting price, regional variations, financing impact
- Success metric: Answers all related prompts in cluster clearly
The brief focuses on answering specific questions rather than ranking for keywords.