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Prompt creation is the most important part of any AEO strategy. Every downstream decision is built on the data you get from the prompts you choose to track. Therefore, the impact of getting this list correct is greater than any optimisation you will make later.
This guide draws on our experience building AEO programmes for 10+ B2B technology companies (read a full case study on how we increased AI visibility by 13x in under 3 months). The guide walks through how to build a prompt set that produces useful signal for B2B companies tracking visibility in LLMs like ChatGPT, Claude, and Gemini.
Published: 06 Aug 2026
Author: Adam Grant
The prompts you track are the foundation of your AEO strategy, for two main reasons.
Before you build a prompt list, you need to move away from traditional SEO thinking. Prompt tracking is not keyword tracking and treating it that way will lead you to the wrong conclusions.
It’s important to remember buyers can ask the same question in countless ways, threads build context across multiple turns, and each user brings their own search history. Prompts are also probabilistic: ask an LLM the same question three times, and you’ll get three slightly different answers. So, appearing in a tracked prompt once doesn’t guarantee you’ll appear every time, and vice versa.
More importantly, a high topline visibility percentage doesn’t necessarily mean you’ll be filling your pipeline. You could have 10% visibility and a full inbound pipeline, or 75% visibility and zero qualified leads. Visibility on your tracked set tells you where you show up in what you’re tracking, not across every possible query.
So don’t get too attached to the top-line visibility number or a specific prompt. Treat prompts as a way to understand general themes, spot who’s winning specific categories, and see what those winners are doing to earn those recommendations.
The best prompt lists are built from real buyer language. Four sources cover most of it:
Pull the actual phrasing prospects use when describing their problem.
Ask what questions they get on discovery calls. Ask which competitors come up unprompted.
r/cybersecurity, r/devops, r/sysadmin, r/saas, and vertical Slack communities are a treasure trove for real evaluation questions and pain points.
It’s important to understand both the high-volume commercial keywords and the total search demand for a category. People prompt differently than they search traditionally, but using search volume directionally allows you to understand where value is.
Once you have raw prompts, the way you organise them determines how useful the data is. A flat list of 100 prompts tells you almost nothing. The same 100 prompts, tagged and clustered, can inform where and how to invest.
Money prompts are the ones where a buyer is actively comparing or choosing. In tech, these follow predictable patterns:
Best X for Y: “Best MDM for remote-first startups”
Alternatives to X: “Alternatives to Okta”
Best X integrations: “Best automated AR for NetSuite”
Best X that is compliant: “Best policy management software compliant with GCC High”
X for [segment]: “Best observability tools for enterprise Kubernetes”
Short-tail prompts (“best CRM”) and long-tail prompts (“best CRM for a 15-person cybersecurity consultancy in the EU”) pull from different source pools. Short-tail leans on high-authority lists and directories. Long-tail leans on niche blogs, Reddit threads, and vendor comparison pages. Tracking both tells you where you have coverage gaps. For more information on source differences between prompt lengths, read our 2026 IT and Cyber AEO Report.
Group prompts by product line, service, use case, or market. Clustering does three things:
Add two dimensions to every prompt:
Funnel stage: discovery, research, comparison, evaluation.
Persona: A CFO evaluating billing software asks different questions than a controller.
The majority of tracking tools let you monitor sentiment prompts. Use these to track questions about quality, value, support, and reliability. As well as head-to-head comparisons vs competitors.
One caveat: LLMs rarely speak negatively about vendors by name. The value is in pulling the actual response text and comparing how your brand is being described versus competitors. If you can connect your tracking tool to Claude or another LLM via MCP, you can automate sentiment reports from the raw responses rather than relying on a summary metric. This will allow you to see how your brands being described, and what sources are driving that. This way you can effectively make changes if needed.
Not every prompt is worth the same. Attach metadata so you can weigh the data properly:
This is the AEO equivalent of blending search volume with commercial intent. It’s easy to cling to a topline visibility %, when in reality some prompts are much more commercially valuable.
For most B2B tech businesses, 100 prompts per country is enough to produce a reliable signal.
Scale up when you have:
There’s a real cost trade-off. Prompt tracking costs roughly $1 per prompt on lean tools and $2–$5 per prompt on premium platforms. More prompts only make sense if you have the team to act on the data. A one-person AEO function tracking 500 prompts will have more visibility and data but won’t have the capacity to act on it. A three-person team can justify a larger, more segmented set.
For most tech categories, the same competitors show up across English-speaking markets, so you don’t need to duplicate your entire prompt set per country. Where localisation matters:
Regulated categories: banking software, healthcare, legal tech, compliance tooling (where local rules surface different vendors)
Local directories and review sites: these get cited differently by region.
For non-primary markets, run the tracking quarterly rather than daily. The competitive set doesn’t shift fast enough to justify the cost.
Prompt measurement is one facet of overall AEO measurement. For a full breakdown, read our measurement blog here: How to Measure the Impact of AI Search for B2B Businesses
In terms of prompt measurement, make sure you are tracking:
Ignore single-prompt swings. Answers are probabilistic, so one run, on one prompt, will not accurately track how visible you are. Look at trends across a cluster over time.
For tech, prioritise by user volume and buyer behaviour; we recommend tracking:
ChatGPT: largest user base, highest impact for most categories.
Google AI Overviews: captures organic search intent at scale.
Perplexity: heavily used by technical evaluators.
Claude: growing fast in engineering and technical decision-maker segments.
Gemini: worth tracking, especially for Google Workspace-adjacent categories.
You’ll need a prompt tracking tool. (There are hundreds)
What to look for: the lowest cost per prompt tracked, and the ability to connect to Claude or another LLM via MCP so you can pull the raw data and analyse it yourself.
What to ignore: “AEO on autopilot” features that promise to diagnose your content gaps and auto-generate content to fix them. This output will destroy your domain authority by producing commodity content that adds no new value to the SERP.
Don’t forget the value is in the data. Every other feature is a wrapper you’re paying a premium for. Push as much of your budget as possible toward tracking the number of prompts you actually need, then use Claude or another LLM to pull out what you need to do to analysis on top.
If you’re building your prompt set, work in this order:
Prompts are the foundation of your AEO programme; spend the time to get the list right.
Visibility Wins builds AEO prompt sets alongside the strategy itself, grounded in the language buyers actually use rather than what internal teams assume they ask. That means pulling real phrasing from sales calls, Reddit, and competitor research, clustering prompts by product, persona, and funnel stage, and attaching business-value metadata so the list reflects what actually drives revenue.
The goal is a prompt set that produces honest signal from day one: clear on which questions are worth winning, and structured so the data can be trusted as an input to real decisions rather than a dashboard for its own sake.
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For most B2B businesses, 100 prompts per country produces a reliable signal. Scale up if you have multiple products, integrations, or distinct market segments (SMB vs. enterprise) that surface different competitors. More prompts only pay off if you have the team to act on the data.
Keyword tracking measures rank for a specific query. Prompt tracking measures probabilistic visibility across a set of representative questions. LLM answers vary run to run, and real users phrase questions in unlimited ways with unique context. Prompt tracking gives you directional signal on where you appear and who’s winning.
Money prompts are commercially relevant prompts that an LLM surfaces a vendor by name. Tech examples: “Best MDM for remote-first startups,” “CrowdStrike vs SentinelOne,” “Alternatives to Okta,” “Snowflake pricing for mid-market.”
Prioritise ChatGPT, Google AI Overviews, Perplexity, Claude, and Gemini. ChatGPT has the largest user base. AI Overviews captures organic search intent. Perplexity is heavily used by technical evaluators. Claude is growing quickly among engineering and technical decision-makers. Gemini matters most for Google Workspace-adjacent categories.
Roughly $1 per prompt on lean tools and $2–$5 per prompt on premium platforms. A 100-prompt set typically runs $100–$400 per month. Push as much of your budget as possible toward tracking capacity rather than “AEO on autopilot” features; the value is in the raw data, which you can analyse with Claude or another LLM via MCP.
Only where it materially changes the competitive set. For most tech categories, English prompts return the same competitors across markets. Localise when you sell into regulated categories (banking, healthcare, compliance) where local rules surface different vendors, or when you’re actively selling in non-English-speaking markets. Run localised tracking quarterly, not daily.
Review the list quarterly. Add prompts as new products, integrations, or competitors emerge. Remove prompts that consistently return non-vendor answers. The core money-prompt patterns (best X for Y, X vs Y, alternatives to X) stay stable; the specific X and Y values change.
Growth Manager, Visibility Wins
Adam Grant leads the Answer Engine Optimisation (AEO) team at Visibility Wins. Adam specialises in helping B2B IT companies become discoverable across Google and LLMs. His work focuses on building the content and authority signals that get brands recommended as trusted answers.