Prompt Research vs Keyword Research: How to Find What People Actually Ask AI

Keyword research tools answer the wrong question for AI search. They tell you what people type into Google search bars, where queries are short and lexical. AI assistants get long, conversational, multi-clause questions. The two distributions barely overlap. If you’re planning AI-search content from a Google keyword list alone, you’re optimising for the wrong vocabulary.

Why prompt language differs from keyword language

Three structural differences:

1. Length. Average Google query: 3 to 4 words. Average ChatGPT prompt: 12 to 25 words. Prompts contain context, constraints, and follow-ups inline.
2. First-person framing. ‘How do I set up Bing Webmaster Tools for my SaaS startup’ vs the keyword ‘bing webmaster tools setup’.
3. Multi-intent. Prompts often ask for comparison, recommendation, and instructions in one query. Keywords decompose these into separate searches.

4 sources of prompt data you can mine

1. Reddit and forum questions. The most accessible proxy. Questions in r/SEO, r/marketing, r/SaaS are written in the same conversational register as AI prompts. Scrape titles weekly.
2. Quora and Stack Exchange. Question-shaped data with built-in upvote signals.
3. YouTube comments and questions. Especially under tutorial videos in your niche. Comments often reveal the gaps a tutorial didn’t address.
4. Your own AI assistant logs. If you operate any chatbot or support assistant, the logs are gold.

Free tools that surface AI-style queries

• People Also Ask (Google): Closer to prompt language than head terms. Pull at scale with the AlsoAsked API.
• Reddit Search: Filter by recent posts in target subreddits, sort by upvotes.
• AnswerThePublic: Visualises question-shaped queries by intent type.
• ChatGPT itself: Ask ‘What are the 20 most common questions someone planning X would ask you?’ and audit the output.

How to convert prompt research into content briefs

1. Cluster 50 to 100 raw prompts into 8 to 12 themes by intent.
2. For each theme, identify the canonical question (the one most users would phrase that way).
3. Phrase one H2 in your content as that exact canonical question.
4. Lead the section with a 1 to 2 sentence direct answer.
5. Expand below with depth, examples, and citations.
6. Add an FAQ block at the end re-asking 3 to 5 of the cluster variants.

Where keyword research still wins

Three contexts where Google’s keyword tools beat prompt research:

• Volume estimation. Google’s keyword databases are still the best volume estimators because they have the data. AI search engines don’t publish query volumes.
• Commercial intent terms. Bottom-funnel transactional terms (‘best CRM for SaaS’, ‘pricing for tool X’) are still mostly searched in Google. AI assistants get more research and exploratory queries.
• Brand and product terms. Branded search behaviour is more reliably tracked through Google than through AI prompt logs.

Combine: use keyword tools for volume validation and commercial intent. Use prompt research for content angle and tone.

A 30-minute weekly prompt audit

1. Pull the top 100 questions in your top 3 subreddits this week.
2. Run each through your tracking tool to see if AI engines already cite you.
3. Identify 5 questions where competitors get cited but you don’t.
4. Add those questions to your editorial calendar as either new content or FAQ additions to existing pages.
5. Re-check in 4 weeks.

This loop, run weekly, will outperform any monolithic ‘AI SEO strategy’ planning exercise.