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

*AEO · Published 2026-05-11 · 15 min read · By Freelancer Tamal*

> Classical keyword research breaks for AEO. Users ask LLMs in full sentences, with context, follow-ups and constraints. Here's how to build a 200-prompt map that mirrors how buyers actually talk to ChatGPT.

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?

**Quick answer:** 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.

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