User Prompt Collection and Question Clustering: 100-500 Real Prompts for GEO

Q: How many real prompts do we need?

Start at 100 and treat 500 as the saturation line. 100 prompts cover the core question skeleton of one niche category; 300-500 support a mid-sized company's content matrix and FAQ system. Beyond 500, new prompts mostly fall into existing clusters and add little marginal value.

Q: Where can we get real user questions fastest?

First-party channels are fastest: customer service and pre-sales chat logs, on-site search logs and website inquiries first; then Zhihu, Xiaohongshu, vertical communities and e-commerce 'Ask Everyone' sections; plus search-engine suggestion words and AI engines' 'people also ask' suggestions.

Q: Do we need AI tools for question clustering?

No. Start with a manual round of topic-intent-frequency tagging, then use LLMs such as Doubao, DeepSeek or Qwen for batch clustering, and finish with human review. Manual work is fine for small samples (100-300); LLMs speed up large samples but always keep a human validation step.

Q: How is a collected question different from a traditional SEO keyword?

Keywords are words; prompts to AI are sentences. AI questions are longer and carry context and constraints, such as 'what CRM should a 10-person SaaS startup already using HubSpot for email choose?' They carry far more semantic weight and directly determine answer-centric content hit rates.

Q: How do clustering results become GEO content?

Prioritize clusters by frequency x intent: high-frequency informational questions become FAQ and knowledge-center entries, high-frequency decision questions become answer-centric comparison pages, and long-tail items roll into the content matrix. After publishing, re-feed the original questions to Doubao and Qwen to test whether your brand is cited. *Data note: the 680M AI search MAU figure and the 19.1% YoY decline in traditional search usage come from QuestMobile data reported by Sina Finance (2026-01-27) and QuestMobile's 2026 AI Application Market Half-Year Report; AI queries being 2-3x longer than traditional queries is SparkToro data compiled by Cintra (2026); Bain & Company research shows 68% of AI users use it to research and summarize and 42% for shopping recommendations; CNNIC's 57th report counts 602M generative AI users in China.*

"How do users actually ask about our brand in AI engines?" Sina Finance reported January 27, 2026, citing QuestMobile: AI search engines now reach 680 million monthly active users in China, and QuestMobile's 2026 AI Application Market Half-Year Report shows traditional search usage per user fell 19.1% year over year in May 2026. Users hand decisions to AI; brands get cited only if question-matching content exists.

Format matters. SparkToro data compiled by Cintra shows prompts to AI run 2-3 times longer than traditional queries: "CRM recommendation" becomes "what CRM should a 10-person SaaS startup already using HubSpot for email choose?" These context-rich prompts are GEO's ammunition. Seven channels and a four-step method follow.

Why "how users ask" is GEO's first-order data

Traditional SEO optimizes keywords; GEO optimizes the questions users ask AI. Every prompt is a content-demand sample carrying intent, context and budget constraints.

Bain & Company research shows 68% of AI users use AI to research and summarize information, and 42% ask for shopping recommendations. Being cited means presence at the decision point. QuestMobile's Q1 2026 AI Application Insights (April 21, 2026) counts Doubao at 345 million MAU; CNNIC's 57th report, 602 million generative AI users.

This shift makes question data the input for content production: teams measure from real prompts what users ask most. Questions are demand; frequency is priority — that's question clustering (see answer-centric content optimization).

Seven channels to collect 100-500 real prompts

Treat "questions users have asked" as an asset: 100 prompts as the starting point, 500 as the saturation line.

Channel	Question type	Volume	Notes
Customer service & pre-sales chat	First-party, high intent	1-3 per chat	Anonymize, strip identities
On-site search & website inquiries	First-party, purchase intent	Dozens monthly	Connect with analytics
Zhihu / Xiaohongshu / communities	Second-party, real scenarios	Dozens per topic	Note date and heat
Search suggestions & related searches	Second-party, wide coverage	5-10 per keyword	Prefer question stems
E-commerce "Ask Everyone" & reviews	Second-party, transactional	5-20 per product	Pain points from reviews
AI engines' "people also ask"	First-party, frontier	3-8 per topic	Doubao, Yuanbao, Qwen, DeepSeek
Competitor FAQs & help centers	Benchmarking	20-50 per site	Find gaps competitors miss

Follow four rules: deduplicate and normalize (merge different phrasings), tag source and date, anonymize (no names), and tag intent (informational, comparative, decision, transactional). Compliance: chat logs are personal information; authorize and anonymize before use per China's PIPL.

Four-step clustering: from scattered prompts to a content map

With 100-500 raw prompts, cluster in four steps to produce a question-to-content mapping.

Step 1 — Normalize. Rewrite colloquial prompts as standard questions ("is this thing expensive" → "what is the price of product X?"), merge identical ones, count frequency.

Step 2 — Layer by intent. Tag each question by decision stage: informational, comparative, decision or transactional.

Step 3 — Cluster and weight by frequency. Group by topic (brand, product, price, service, competitor comparison), batch-cluster with LLMs, review manually. Rank by frequency x intent; high-frequency first.

Step 4 — Map outputs and test live. High-frequency informational questions become FAQ and knowledge-center entries (see enterprise AI knowledge base guide); decision questions become answer-centric comparison pages; long-tail items enter the content matrix (GEO content depth guide). After publishing, re-feed the questions to Doubao and Qwen to check citation.

In one anonymized case (industry observation), a B2B equipment company collected 260 prompts from sales, service and community logs. Clustering showed "lead time," "repeat-purchase discount" and "old equipment disposal" accounted for 41% of questions, none covered on the website. After publishing FAQ and comparison pages, AI-answer brand mentions improved. This loop ties clustering to the zero-click era: brands must appear inside AI answers, not links (see zero-click search GEO strategy).

FAQ

Q1: How many real prompts do we need?

Start at 100, 500 as the saturation line. 100 covers a niche's core question skeleton; 300-500 supports a mid-sized company's content matrix. Beyond 500, new prompts fall into existing clusters.

Q2: Where can we get real user questions fastest?

First-party channels are fastest: sales and service chat logs, on-site search logs and inquiries; then Zhihu, Xiaohongshu, communities and e-commerce "Ask Everyone."

Q3: Do we need AI tools for question clustering?

No. Tag topic-intent-frequency manually, use LLMs for batch clustering, finish with human review. Manual works for small samples; keep human validation for large ones.

Q4: How is a collected question different from a traditional SEO keyword?

Keywords are words; prompts are sentences. AI questions are longer, carry context and constraints, and directly determine answer-centric hit rates.

Q5: How do clustering results become GEO content?

Prioritize clusters by frequency x intent: informational questions become FAQ and knowledge-center entries, decision questions become answer-centric comparison pages. After publishing, re-feed the questions to Doubao and Qwen to test citation.

Turning "how users ask" into content input is GEO's basic skill in 2026. Shanghai Zheming offers user-question research, question clustering and answer-centric content in one service. Call +86 18917757529 or email jaysun@widesight.cn.

Written by Zheming Digital Communication Research Institute.