Most teams get AEO (Answer Engine Optimisation) wrong at the very first step. They jump into content or tools without defining what they are actually measuring.
Prompt curation determines whether your AEO programme produces insight or noise.
Prompt design is easy. Asking the right questions is the part that is hard. If you don't curate your prompt list to the important questions your business involves, your AEO data won't be useful.
In this article, we will be breaking down prompt list curation: Why your prompt pool may not correctly reflect your brand, what topics should your prompt pool include, and where can you find inspiration to get prompts that reflect real user queries.
When HubSpot built its AEO practice, their breakthrough did not come from publishing more content. It came from building a structured prompt library from hundreds of prompts, across products and buying stages. That shift turned vague visibility into something measurable and, more importantly, actionable.
An AEO prompt is a real question a user might ask an AI answer engine that relates to your product or service. For example, “Which fllorist shop is best in Singapore?” is a prompt.
HubSpot defines AEO prompts as the questions people ask when searching for brands or products in AI systems. These are not keywords. They are complete questions that reflect genuine intent.
Three characteristics matter:
Curating a prompt list means building a deliberate, structured set of real user questions that you will track across AI engines.
This is not brainstorming. It is selection, validation, and organisation.
A well curated prompt list does three things:
If your prompt list is shallow or skewed, your entire AEO dataset will be misleading.
Most prompt lists fail because they are either too generic or too narrow.
A typical mistake looks like this:
A firm providing SaaS HR software in Singapore may track prompts like “What is HR software?” and “Benefits of HR systems”. These are awareness level questions in AI answers, which rarely mention businesses or brands in your specific niche. Hence, your brand visibility in these prompts are often low to zero.
The more important commercial questions to your niche are missing here:
Across all of these, the pattern is consistent. Awareness questions are overrepresented because they feel safe and familiar. Decision-stage questions are underrepresented because they require specificity and commercial clarity.
Users on AI search tools are usually searching high-intent prompts, specific to their situation. It means deliberately including the uncomfortable, high-intent questions where comparison, pricing, and commitment are explicit.
The most important rule is coverage. Users ask different questions at different stages, so your prompts must span all of them.
Case Study: We had a high mix, low volume paper product manufacturer come to us for consultation on AEO. They make a wide range of paper based products in relatively small batches. Their business often handles custom orders, different specifications, and frequent changes in size, material, print, or finish.
If you have only awareness stage prompts, you will miss out on buyers with higher purchasing intent. Oftentimes, it is smarter to include a healthy portion of Consideration and Evaluation level prompts if your business is a niche, as that is usually where decisions are being made.
Strong prompt lists are grounded in real behaviour, not internal opinions. Use real data, not workshop guesses. Do not sit in a meeting room discussing and inventing prompts.
The most reliable sources are:
The most reliable way to curate prompts is to organise them across two dimensions: topic clusters and buying stages.
Each cluster represents a commercially meaningful theme.
For example, if you are providing educational services in Singapore:
These are not content ideas, they are tracking containers in areas that you want to be seen in AI search.
Each cluster should include prompts from all four stages:
If one stage is missing, your dataset has a blind spot.
In practice, most teams over-index on awareness and under-invest in decision-stage prompts, which is where revenue actually sits.
AI-generated answers fluctuate between each generation.
Once we consolidate a substantial amount of prompts per topic, we can ensure a few things:
Think of it like soil sampling. Five samples will not tell you how the whole field performs.
A handful of prompts will cause your visibility to swing wildly based on small changes. Widen that pool and patterns become visible, outliers matter less, and gaps are easier to diagnose.
Branded Prompts are prompt questions that include your brand name.
In the earlier example, “Is [manufacturer name] suitable for custom paper packaging?” is a branded evaluation level prompt.
Including a small mix of these prompts will let you know how AI search platforms are actually describing or rating your brand. Usually a few or a handful of them is enough for you to check visibility (10-20% of your prompt library), just be sure to tag them as branded so you can exclude them if you wish to see your accurate brand visibility.
Take caution when using these, because not every repository is well maintained or safe. Check the source carefully, watch out for fake links, and avoid downloading anything that looks unreliable. Also, some may say they are free at the start, but require fees to upgrade once you hit a wall in usability.
One reason HubSpot’s approach scales is its use of CRM data to shape prompt generation.
Instead of guessing, the system pulls from:
This produces prompts that are closer to real buying scenarios.
It also closes the loop. Prompt performance feeds directly into content recommendations, which can be executed inside the same platform. That removes the usual disconnect between insight and action.
Try HubSpot AEO Tool for free today!