How to Do Prompt Research for Generative Engine Optimisation (GEO)

Prompt research is the process of identifying and analyzing the conversational queries people use when talking to AI engines like ChatGPT, Perplexity, Claude, and Google's AI Overviews. Unlike traditional keyword research that focuses on short search terms, prompt research centers on full questions and natural language conversations.

Your goal is to understand exactly how your target audience phrases questions about your industry, product, or service when talking to AI assistants. This understanding lets you optimize your content to appear in AI-generated responses—the new SERP.

Here's what happens in a typical AI response: 8-10 sources get cited at the bottom, and 5-6 brands get mentioned in the actual answer. But getting cited isn't the same as getting mentioned. Citations are those reference links, while mentions are when the AI actually discusses your brand in its response.

I analyzed 3,000 pages that consistently appear in AI results, and the biggest ranking factor is clear: how closely your page title and meta description match what the user actually asked. It's semantic matching on steroids.

How do AI engines surface and cite content?

Understanding how AI engines work helps explain why you need to shift your optimization strategy. Here's the process:

Query processing: When someone asks a question, the AI engine analyzes intent and searches for relevant information from its training data and real-time sources.

Content evaluation: The engine evaluates potential sources based on relevance, authority, and how directly the content answers the specific question.

Response generation: The AI crafts a conversational response, weaving together information from multiple sources while deciding which ones deserve citations.

Citation selection: Sources that most directly address the user's specific question get priority for citations and mentions.

The key insight: AI engines favor content that sounds like it was written to answer the exact question being asked. Your old keyword-stuffed pages won't cut it.

What are the best methods for finding valuable prompts?

Here are seven proven methods I use to uncover the prompts that matter most:

1. Mine conversational queries from Google Search Console

Your Search Console already contains long-form, conversational queries that mirror AI prompts. Use this regex filter to surface them: ([^" "]*\s){10,}?

This pulls queries with 10+ words—the detailed questions people ask AI engines. Instead of seeing "project management software," you'll find "what project management software works best for remote teams with tight budgets?".

2. Extract questions from Reddit and community forums

Reddit exposes raw user language around problems, comparisons, and solutions. Communities like r/entrepreneur, r/SaaS, and industry-specific subreddits are goldmines for natural question patterns.

Tools like Radix and keywordspeopleuse.com can surface these at scale, but I often find manual browsing reveals the most valuable, specific prompts that competitors miss.

3. Farm prompts from sales conversations

If you are a sales led product, use the transcripts from sales calls to find how they frame their questions. Every "Can your tool do X?" or "How does this compare to Y?" represents a potential AI query.

Tools like Granola can automatically extract recurring questions from call transcripts, giving you prompts straight from your target audience's vocabulary.

4. Query AI engines directly

Skip the guesswork and ask AI engines what people want to know. On Perplexity, type any industry question and note the suggested follow-ups. On ChatGPT, try: "What are the most common questions people ask about [your category]?"

These suggestions often reflect real user patterns and can reveal prompts you hadn't considered.

5. Reverse-engineer competitor visibility

If competitors consistently appear in AI responses, you can infer the prompts behind those citations. See a competitor page titled "Best Customer Support Software for Small Teams"? The prompt was likely "What's the best customer support software for small teams?"

Tools like Radix show which competitor pages get cited across ChatGPT and Perplexity, helping you map citations to likely prompts and identify content gaps.

6. Analyse support and chatbot data

If your clients run customer support or have AI chatbots, they already own valuable prompt data. Every user query is practice for how they'd phrase the same question to ChatGPT.

Export logs, cluster similar questions, and turn frequent queries into content optimization opportunities. Their users are literally telling you what they want to know.

7. Study People Also Ask and question aggregators

Google's People Also Ask boxes reveal how users naturally phrase questions. Quora and StackExchange are particularly valuable for evergreen, conversational Q&A that AI engines love to cite.

For B2B and technical niches, StackOverflow-style prompts are especially valuable since they mirror how technical users ask detailed questions.

How should I optimize my content for prompts?

Here's how to transform your optimization strategy for the prompt era:

Match titles to prompts exactly If people ask "What's the best CRM for small teams?", make that your H1. Don't optimize for "CRM software for SMBs"—use the exact language from the prompt. AI engines reward semantic precision.

Write meta descriptions like AI responses Craft meta descriptions that sound conversational and directly answer the implied question. Instead of "Learn about CRM features and pricing," try "Most small teams need CRM software that costs under $50/month and includes contact management, email integration, and basic reporting."

Structure content to answer immediately Don't bury your answer in paragraph three. Lead with the direct response to the prompt, then provide supporting detail. AI engines favor content that answers questions upfront—think featured snippet optimization but for conversational queries.

Use prompt language throughout your content If users say "small teams," don't switch to "SMB organizations" in your content. Maintain the same vocabulary and phrasing patterns as the original prompts. Consistency matters more than variety.

Create content for prompt clusters Group related prompts and create comprehensive content that addresses multiple variations. A single piece about "CRM for small teams" might target prompts about pricing, features, comparisons, and implementation.

Optimize for conversational search patterns Write in a natural, conversational tone that mirrors how people ask questions. Avoid overly formal or keyword-stuffed language that sounds robotic when read aloud.

Document and track your prompt research Keep detailed records of which prompts you're targeting, which content addresses them, and how performance changes over time. I use a simple spreadsheet with columns for prompt, target content, AI engine visibility, and notes.

Test across multiple AI engines Different AI platforms may respond better to different content styles or formats. Test your optimized content across ChatGPT, Perplexity, Claude, and Google AI to see where you get the best visibility.

Monitor and adapt to prompt evolution Language patterns change, and new prompts emerge as industries evolve. Set up monthly reviews to refresh your prompt research and stay current with how your audience asks questions.

Collaborate with other teams Sales, customer support, and marketing teams all interact with customer language. Regular collaboration ensures you're capturing the full spectrum of how your audience phrases questions and problems.

Which tools can help me scale prompt research?

Here are the tools I recommend for different aspects of prompt research:

Radix: Shows competitor citations across AI engines and helps map them to likely prompts. Particularly useful for understanding which content types and topics get the most AI visibility in your space. Essential for competitive analysis.

Granola: Automatically extracts questions and themes from sales call recordings. Perfect if you work with B2B clients who have regular prospect conversations.

Alsoasked & keywordspeopleuse.com: Aggregates questions from Reddit, Quora, and other community sources at scale. Great for finding natural language patterns you might miss manually.

Google Search Console: Use regex filters to identify existing long-form queries that mirror AI prompts. Your best starting point since this data is free and directly relevant to your site.

AI engines themselves: ChatGPT, Perplexity, and Claude can all suggest related questions and reveal prompt patterns when asked directly. Sometimes the best tool is just asking the right questions.

AnswerThePublic: While primarily for traditional SEO, it's great for finding question-based queries that translate well to prompts.

The key is combining multiple sources to build a comprehensive understanding of how your audience naturally asks questions about your industry, product, or service.

What does the future hold for prompt research?

Prompt research isn't just a trend—it's the foundation of visibility in an AI-driven search landscape. As AI engines become more sophisticated and widely adopted, marketers who master conversational optimization will have a significant competitive advantage.

The shift from keywords to prompts represents a fundamental change in how people discover information. Instead of typing "CRM pricing," they're asking "How much should I expect to pay for CRM software for my 15-person team?" The marketers who recognize this shift and optimize accordingly will dominate AI search results.

My advice: start with one or two of the methods outlined above, focus on your most important customer questions, and gradually expand your prompt research as you see results. The future of search is conversational—and that future is already here.

Don't wait for your competitors to figure this out. The early movers in prompt research will build advantages that become harder to overcome as AI search adoption grows.