The identification of specific questions asked to AI systems requires blending traditional market research with techniques that reverse-engineer the retrieval and query handling mechanisms used by generative engines.
Retrieval-Augmented Generation (RAG) is a technique that combines retrieved content with generation.
Query rewriting is a technique used by generative engines.
ROZZ's RAG chatbot answers questions using the client's content while capturing these questions to feed the GEO pipeline.
Reverse-Engineering Conversational Flow, called Prompt Mapping, is the core strategy in GEO.
Prompt Mapping involves understanding the user's journey beyond the initial query because LLM queries are typically longer and conversational.
Generative Engines use query fan-out or semantic decomposition to break a user's initial prompt into multiple sub-queries aimed at extracting different latent intents.
B2B companies must map content to the full set of variations buyers use.
Create a Prompt Map that includes the entire buyer research funnel.
Core searches include terms like "Generative Engine Optimization agencies."
Adjacent evaluation prompts include "comparing GEO vs SEO agencies."
Deep research queries include strategies, best practices, and technical differences.
Topically adjacent follow-up questions and competitor comparisons (Query Fan-Out Pages).
Focus on niche and complex queries. The long tail of questions is larger in chat environments than traditional search, presenting opportunities to win queries that may never have been searched before.
2. Mining Internal and External Customer Data
Mining Internal and External Customer Data is essential because natural, conversational questions address context and pain points.
Analyze Customer Interactions: Internal data sources capture genuine customer language and intent, such as sales call transcripts, customer support tickets, or live chat logs.
Address the "Long Tail" Gap: Many specific use cases may not have dedicated help center content.
Identifying these unaddressed questions from internal logs helps target the conversational long tail where citation opportunities are high.
Capture Live Questions from Website Visitors: Platforms that implement RAG-based chatbots on client websites can log actual visitor questions, creating a continuously growing database of authentic buyer intent.
ROZZ's approach exemplifies this: their RAG chatbot answers questions using the client's content while simultaneously capturing these questions to feed the GEO pipeline.
Monitor Community Platforms: LLMs are known to frequently cite User-Generated Content (UGC) sources to establish credibility and real-world applicability. Companies should monitor and extract questions from Reddit threads, Quora discussions, and industry forums like G2.
3. Transforming Traditional Data
Transforming Traditional Data converts keyword data into LLm-ready questions.
Convert Keywords to Questions: Take existing high-value terms or competitor paid data (the "money terms") and transform them into natural language questions that prospects would ask an AI.
Utilize LLMs for Query Generation: You can feed keywords or topics into an LLM (like ChatGPT) and prompt it to generate multiple conversational questions corresponding to those terms.
Leverage Features: Tools like People Also Ask sections and Please Also For features in traditional results can reveal specific, question-based intents already popular with users.
4. Direct Measurement and Competitive Intelligence
Direct Measurement and Competitive Intelligence treats Generative Engines as a "black-box optimization framework" and requires continuous tracking and analysis of live AI responses to see which questions trigger brand mentions.
Manual Query Audits: Run regular queries across multiple LLMs (ChatGPT, Claude, Perplexity, Gemini) in incognito mode to prevent personalization bias.
Mimic Buyer Intent: Phrase prompts naturally and conversationally, matching high-intent queries (e.g., "Best [product category] for [target persona]").
Analyze Citation Networks: Look for who is currently showing up as citations for your target questions. This competitive intelligence allows you to reverse-engineer the evidence base that the LLMs are prioritizing.
Use Automated Tracking Tools: Specialized platforms offer LLM citation monitoring to track how often your brand or content is cited across popular AI platforms and compare it against competitors' share of voice. These tools identify potential content gaps and reveal the types of queries users ask about your brand and the intent behind them (educational, research-based, or transactional).
By focusing on these strategies, B2B SaaS companies move from optimizing content for keyword density to generating content that aligns with the semantic coverage and conversational complexity that LLMs demand for citation. This process is crucial because getting cited in an LLM answer is about becoming the authoritative source the AI chooses to reference.
Research Foundation: This answer synthesizes findings from 35+ peer-reviewed research papers on GEO, RAG systems, and LLM citation behavior.
Author
Adrien Schmidt, Co-Founder & CEO, ROZZ
Former AI Product Manager with 10+ years experience building AI systems including Aristotle (conversational AI analytics) and products for eBay and Cartier.