How Do You Build a Prompt Set for Tracking AEO?

## The short answer

To build a prompt set for AEO tracking, gather the real questions your buyers ask AI assistants, group them by intent (category, comparison, branded and problem-led), prioritise the ones closest to a purchase decision, and write them in natural language the way a person actually types or speaks. A good starting set is 50 to 200 prompts, refreshed as your market and buyer language evolve. The prompt set is the measuring instrument — get it wrong and every downstream number is wrong too.

## Why the prompt set is the whole game

In AEO you cannot measure everything an assistant might say, so you sample. The prompts you choose define what you can see. A set skewed toward easy, brand-aware questions will report flattering numbers and hide your real gaps. A set built around genuine buyer intent gives you an honest, actionable picture. Treat building it with the same rigour you would a research survey.

## The four intent layers

A balanced prompt set covers four kinds of question:

1. **Category / discovery** — broad questions where buyers first learn the landscape: "what is the best way to do X", "tools for Y". You want to be present here to enter the consideration set.

2. **Comparison / evaluation** — "X vs Y", "alternatives to Z", "is X worth the price". These are high-intent and often decisive; assistants frequently produce shortlists here.

3. **Branded** — "what does [your brand] do", "is [your brand] reliable", "[your brand] pricing". These reveal how accurately and favourably assistants describe you.

4. **Problem-led** — questions framed around a pain point rather than a product: "how do I stop X from happening". These catch buyers earlier and reveal whether you surface as a solution.

Most teams under-invest in comparison and problem-led prompts, which is exactly where purchase decisions form.

## Where to source real questions

Do not invent prompts from your own vocabulary. Pull them from where buyers actually express need:

- Sales and support call notes — the questions prospects ask in their own words.

- Your site search and existing search-query data.

- Community forums, review sites and social threads in your category.

- Customer interviews and win/loss analysis.

- The "people also ask" style follow-ups assistants themselves suggest.

The goal is to capture buyer language, not marketing language. Buyers rarely use your product category's official name.

## Writing prompts the way people ask

AI assistants respond to natural, conversational phrasing. Reflect that:

- Use full questions, not keyword fragments.

- Include realistic context: company size, industry, constraint or budget where relevant. "Best X for a small charity on a tight budget" tests a very different answer than "best X".

- Vary phrasings of the same intent, because small wording changes can shift answers.

- Keep some prompts deliberately neutral, with no brand in them, so you measure unprompted recommendation rather than confirmation.

## Prioritising and weighting

Not all prompts are equal. Assign each a priority based on how close it sits to a buying decision and how much volume that question likely carries. When you report share of voice, weight by priority so that winning a high-intent comparison question counts for more than appearing in a broad definitional one. This stops a large set of easy prompts from drowning out the few that matter most.

## Keeping the set honest over time

A prompt set is a living instrument:

- **Freeze a core set** for trend continuity, so month-to-month comparisons are valid.

- **Add an exploratory layer** that you rotate to discover new questions and emerging competitors.

- **Review quarterly** as buyer language, your product and the competitive field change.

- **Version it** so you know which set produced which historical numbers.

If you constantly change every prompt, you lose the ability to see trends; if you never change it, you go blind to new buyer behaviour. The frozen-core-plus-rotating-edge approach balances both.

## Common pitfalls

- **Too many branded prompts** — flattering but low-value; assistants almost always describe you when you are named.

- **Marketing jargon** — testing terms buyers never use.

- **No competitor framing** — without comparison prompts you cannot measure share of voice properly.

- **Tiny sets** — fewer than a few dozen prompts and non-determinism swamps the signal.

neart.ai builds enterprise-grade products in this space, and the prompt set is consistently the part teams underestimate and later wish they had built more carefully.

## Practical takeaway

Start by collecting 50 real buyer questions from sales, support and review sources, then sort them into category, comparison, branded and problem-led buckets — aiming for a heavier weight on comparison and problem-led. Write them conversationally with realistic context, freeze a core set for trends, and rotate an exploratory edge to catch what is changing.