What are buyers
actually asking AI?
Enter a topic. The generator fans it out into the real human questions answer engines get asked — grouped into ELI5, Dev/Pro and comparison styles, each scored for how commonly it is framed and how many follow-ups it triggers. It is a taste of the full Topic Seed Workspace.
The questions buyers ask AI, not the keywords they type
AI answer engines are not queried like a search box. People ask them full, conversational questions — and then they ask follow-ups. Getting cited means covering those real questions, in the language buyers actually use, across the whole journey from "what is this" to "which one should I buy". This generator turns one topic into that map: the prompts, grouped by how people speak, each scored for how common the framing is and how many follow-up turns it typically triggers. Instead of guessing which phrasings matter, you start from a structured picture of the conversation your buyers are already having with AI.
Every prompt is classified by intent — informational, commercial, comparative, transactional, troubleshooting or brand-defence — and by buyer-journey stage, from awareness through consideration to decision. That classification is what turns a flat list into a plan: you can see at a glance whether a topic is dominated by early "how does this work" curiosity or late "which one is best" evaluation, and prioritise the content that closes the biggest gap for your brand.
How to use the results
Read the results as a coverage checklist. The three styles map to three real audiences. The Explain-Like-I'm-5 prompts are conceptual and analogical — the questions a first-time buyer asks before they know your category exists. The Dev/Pro prompts are technical, constraint-heavy and specification-driven — the questions a specialist asks when they are close to a decision and want detail. The Side-by-Side Comparison prompts are evaluation questions — "X vs Y", "best option for", "alternatives to" — the moments where a recommendation is won or lost. A brand that only publishes marketing copy tends to cover the first style and lose the other two.
The likelihood score helps you sequence the work. High-likelihood, high-follow-up prompts are where a single strong, citable answer earns the most leverage, because they are both common and the start of a longer conversation. Low-likelihood prompts are still worth covering for completeness, but they are rarely where you start. Used together, the two figures let a small content team spend its effort where the measured conversation is densest rather than spreading thin across every possible phrasing.
What this measures — and what it does not
Prompt generation is deterministic, and the likelihood and follow-up figures are modelled from prompt structure and intent — a transparent, explainable stand-in for query volume, never presented as observed engine behaviour. That honesty matters: no third-party tool can see inside an engine's private query logs, so anyone quoting exact "AI search volume" as fact is guessing with a confident face. This tool tells you what it is modelling and why, so you can act on it without over-claiming.
What the free tool does not do is measure how AI engines actually answer these prompts, whether they cite your brand, which competitors they recommend instead, or how any of that changes over time. That is the measured half of the platform: save these prompts into a monitored set in the workspace, run a sweep across ChatGPT, Claude, Gemini and Perplexity, and every metric from presence rate to citation share to sentiment is archived with the raw answer behind it. The generator is where the plan starts; the workspace is where you prove it worked.
Frequently asked questions
Where do these prompts come from?+
They are generated deterministically from your topic using the same engine that powers IndexHalo's Topic Seed Workspace — templates shaped by real question patterns, then classified by intent and buyer-journey stage. The same topic always yields the same prompts, so you can compare runs and share results without worrying about randomness.
What is the likelihood score?+
A modelled 0–100 estimate of how commonly that exact framing is used — shorter, natural, question-led phrasings score higher. It is a transparent stand-in for query-log volume, clearly labelled as modelled rather than observed engine data, so you can prioritise without over-claiming certainty you do not have.
What does the follow-up count mean?+
Answer-engine conversations rarely stop at one question. The follow-up count is a modelled estimate of how many further turns a user typically takes after an opening prompt — higher for open, educational questions and lower for transactional ones. It tells you which prompts open a longer conversation worth owning end to end.
How is this different from keyword research?+
Keyword tools give you search strings ranked by monthly volume. This gives you conversational prompts — the way people actually address ChatGPT, Claude, Gemini and Perplexity — grouped by how they speak (simple, technical, or comparison) and what stage of the buying journey they are in. It is question research for the AI era, not keyword research.
Which engines do these prompts apply to?+
The generated prompts are engine-neutral question patterns, so they apply across ChatGPT, Claude, Gemini, Perplexity, Copilot and Google's AI answers. In the full workspace you can shape phrasing to each platform's conventions and then measure how each engine actually answers them.
Can I save, measure, and export these?+
Yes — that is what a free account unlocks. In the workspace you can save seeds, promote prompts into a monitored set, measure how AI engines actually answer them, track how that changes over time, and export the batch as CSV or JSON for your content team.