Conversational Query Mapping: Building a 200-Prompt AEO Keyword Plan

Keyword research as we know it was built for blue-link search — short, atomic, intent-classified queries. Conversational AI surfaces don't work that way. Users type 60–200 word prompts with constraints, context, and follow-ups. To win AEO, you need a different artifact: a conversational query map of the prompts your buyers actually use.

Table of contents

1. Why classical keyword research fails for AEO · 2. The 5 prompt archetypes buyers use · 3. Sourcing real prompts (4 reliable methods) · 4. Building the 200-prompt map · 5. Mapping prompts to pages · 6. Tracking and iteration · 7. FAQ

Why does classical keyword research fail for AEO?

Classical keyword tools (Ahrefs, Semrush, Google Keyword Planner) sample short search-engine queries. Conversational prompts are 5–20× longer, contextual, and rarely appear in those datasets. **Optimizing for 'best CRM' misses the actual prompt: 'I run a 5-person consulting firm in Bangladesh, mostly project work, what's the best CRM under $30/user/month that also handles invoicing'.** Page content has to map to the long prompt, not the short keyword.

The 5 prompt archetypes buyers use

1. Constrained recommendation ('best X for Y persona under Z constraint'). 2. Comparison drill-down ('X vs Y for [specific use case]'). 3. Diagnostic ('I have problem A, B, C — what's likely the cause'). 4. Workflow ('how do I set up X for Y goal'). 5. Validation ('is X a good choice if I'm planning to Z'). Map your category's top 40 prompts in each archetype and you have a 200-prompt baseline. Each archetype maps to a different content shape.

Sourcing real prompts (4 reliable methods)

1. Customer interviews — ask buyers what they typed into ChatGPT before booking. 2. Sales call transcripts — most discovery questions started as a prompt somewhere. 3. Reddit + community threads in your category — full-sentence questions with the exact phrasing buyers use. 4. AI engine 'people also ask' / suggested follow-up prompts in ChatGPT and Perplexity. **Don't generate prompts from imagination — sourcing them from real interactions is the entire point.**

Building the 200-prompt map

Spreadsheet columns: prompt, archetype, persona, stage of journey, target page on your site, current citation status (cited / not cited / wrong source), priority. Aim for 200 rows split roughly: 80 constrained recommendations, 50 comparisons, 30 diagnostics, 25 workflows, 15 validations. This becomes the single source of truth for what your AEO program is actually optimizing toward.

Mapping prompts to pages

Each prompt should map to one canonical page. Constrained recommendations → comparison or 'best of' pages. Comparison drill-downs → vs/alternatives pages. Diagnostics → troubleshooting / problem-aware blog content. Workflows → HowTo pages with step schema. Validations → use-case + case-study pages. Pages serving multiple prompts must include the relevant phrasing as H2s and direct answer blocks for each.

Tracking and iteration

Re-run all 200 prompts monthly across ChatGPT, Perplexity, Gemini and AI Overviews. Log: cited or not, source URL, brand mention rank, source freshness. Prioritize next month's content work based on prompts where you're not cited but a competitor is — those are your fastest wins. **The query map is a living artifact, not a one-time research deliverable.**

Frequently Asked Questions

How long does building a 200-prompt map take?

About 8–12 hours of focused work for a small team — most of the time is sourcing real prompts (interviews, transcripts, Reddit). The mapping and prioritization is fast once raw prompts are collected.

Can I use AI to generate prompts instead of sourcing them?

Bad idea — AI-generated prompts skew toward generic phrasing and miss the specifics that make real prompts useful. AI is fine for variation expansion (rephrasing the same prompt 5 ways) once you have a real seed.

How does this differ from 'long-tail keyword research'?

Long-tail keywords are still atomic search-engine queries. Conversational prompts include constraints, persona context, and intent within a single sentence. The unit of analysis is the full prompt, not extracted keywords from it.

Should I prioritize prompts by volume?

No reliable volume data exists for individual conversational prompts. Prioritize by deal size and journey stage — a low-volume bottom-of-funnel prompt outranks a high-volume top-of-funnel one for ROI.

Does this work for ecommerce?

Yes, even better — buyers describe specific use cases ('hiking shoes for someone with flat feet under $150 that work in monsoon'). Map prompts to product collection pages and individual SKUs with rich Product schema.