AI Summary
Keyword research finds the strings people type into Google. Prompt research finds the questions people ask AI assistants. The two barely overlap, and for the first time you can put real numbers on both sides. We pulled live June 2026 data to show exactly where each method sees, and where each one is blind.
Google volume vs AI volume: live data, same keywords
We ran the same keyword set through Google Ads volume data and through DataForSEO’s AI Keyword Data API, which models how often keywords appear in queries to ChatGPT and other LLMs. United States, June 2026, monthly figures:
| Query | Google searches | AI assistant queries | What it tells you |
|---|---|---|---|
| keyword research | 368,000 | 1,298 | Head terms still live in Google |
| best crm for small business | 3,600 | 197 | Commercial terms: Google dominates, AI share growing |
| how to do keyword research | 1,300 | 120 | How-to intent is migrating |
| generative engine optimization | 4,400 | 27 | Trend terms spike in Google first |
| prompt research | 20 | 50 | Asked 2.5x more in AI than in Google |
| best crm for a 10 person startup | no data | 0 recorded | The shared blind spot: long conversational queries |
Three things jump out. AI query volumes run at roughly 0.5 to 5 percent of Google volumes for established terms. Some terms have already inverted: “prompt research” is asked more inside AI assistants than it is searched in Google. And the most valuable queries, the long, constraint-loaded ones, show no volume anywhere, because most prompts appear only once.
Why prompts and keywords are different languages
- Length and structure. A Google query averages 3 to 4 words. A ChatGPT prompt routinely runs 15 words or more, with context, constraints, and budget baked in.
- First person framing. “How hard is it to import all our contacts from a messy Excel spreadsheet” is a prompt. “crm import contacts” is a keyword. Same need, different language.
- One prompt, many retrievals. AI engines decompose a prompt into multiple sub-queries through query fan-out, then synthesize one answer. Your content competes at the sub-query level, which is exactly what we measured in our 42,971 citation study: the engine retrieves short, atomic answers to the decomposed pieces.
What prompt research can and cannot see in 2026
Be clear-eyed about the tooling. No AI provider publishes prompt logs. There is no Google Keyword Planner for ChatGPT. Everything on the market is a model built on proxies:
- DataForSEO AI Keyword Data estimates keyword-level AI search volume with 12 months of trend. It is keyword-shaped, not prompt-shaped, but it is queryable by API today: the table above cost about one cent to produce.
- Semrush Prompt Research builds on a clickstream-informed prompt database and focuses on tracking decision-stage prompts where AI actually recommends brands.
- Sistrix Prompt Research clusters 62 million real user questions into 1.4 million analyzable topics with intent and journey stage. Currently Germany only, which tells you how early this category is.
So is prompt research possible right now? Partially. You can get modeled AI volumes per keyword, clustered topics in some markets, and brand-mention tracking across engines. You cannot get the raw distribution of prompts. The free workflow below fills that gap.
The free prompt research workflow (with live examples)
1. Interrogate the model directly
Ask the assistant what people ask it. We gave Gemini one prompt: “What are the 15 most common questions people ask you when they are choosing a CRM for a small business?” The output is a ready-made list of first-person, constraint-loaded prompts:
Notice the language: “my sales guys”, “messy Excel spreadsheet”, “what happens if we decide to leave”. No keyword tool surfaces that phrasing. Each of those is an H2 or FAQ candidate.
2. Mine People Also Ask at scale
PAA boxes are Google’s own question-shaped data and the closest free proxy for conversational phrasing. Pulling the live PAA set for “keyword research” returns: “What is keyword research?”, “Can I use ChatGPT for keyword research?”, “How to do keyword research for free?”, “What are the 4 types of search intent?”, and “Can I do SEO by myself?”. The ChatGPT question appearing twice in one PAA tree is the market telling you where this topic is heading.
3. Harvest forums where people already write prompts
Reddit, Quora, and Stack Exchange questions are written in the same first-person, multi-constraint register as AI prompts, with upvotes as a free demand signal. Scrape question titles in your niche weekly and treat them as prompt candidates.
4. Cluster prompts into topics and write to the canonical question
- Cluster 50 to 100 raw prompts into 8 to 12 intent themes.
- Pick the canonical question per theme and phrase one H2 exactly as that question.
- Open the section with a direct 1 to 2 sentence answer, then add depth. That structure matches how engines pick citable sentences.
- Prioritize themes with constraints (budget, team size, industry): constraint prompts are where assistants compare options and recommend brands.
5. Track whether AI actually mentions you
Prompt research without response tracking is half a loop. Re-run your 10 to 20 highest-value prompts monthly and log brand mentions across engines, your share of voice against competitors, and the referral sessions landing in GA4.
Where keyword research still wins
- Demand sizing. Google volumes remain the only robust demand estimator. AI volumes are modeled and a fraction of the size.
- Commercial intent with CPC signal. Cost-per-click data tells you what a click is worth. Prompts have no CPC.
- Trend history. Keyword databases go back years; AI volume data starts in 2025.
- Surfaces that are still Google. AI Mode and AI Overviews sit on Google queries, so classic keyword targeting still feeds the AI layer above it, even as clicks decline.
The operating model for 2026: size demand with keywords, phrase content with prompts, and measure both sides with the GEO KPI framework and the AI search metrics that matter. If you are new to the discipline, start with what GEO is.