AI prompt testing is one of the most straightforward things you can do this morning to improve your B2B marketing strategy.
Open ChatGPT, Perplexity, or Gemini right now. Type in the name of your company and ask what it does. Then ask which companies it would recommend for the problem you solve.
Whatever comes back is likely what your next buyer is seeing before they visit your website or fill out a contact form. Of course, you hope that it’s accurate. But all too often, the responses LLMs offer about B2B brands are thin, wrong, or not there at all.
But your target audience doesn’t care, and they are not waiting for your nurture sequence. They are asking an AI.
This guide walks you through a tool-free, expense-free way to do AI prompt testing. It ends by covering what to do if you don’t like the results.
Your buyers are researching you with AI
The B2B pre-sales research phase has changed. Buyers at Series A and growth-stage tech companies are increasingly using AI assistants as a first stop when evaluating vendors. They ask conversational questions that get synthesized answers. It’s those answers that enable them to form a view of the field before a human ever enters the picture.
This matters for B2B tech companies specifically because the buying process is longer, the stakeholder group is wider, and the decisions are higher-stakes. A CFO who asks an AI assistant to summarise what your company does and gets back a vague or inaccurate answer has already started forming the wrong impression. Your SDR will spend the first ten minutes of a discovery call correcting it.
AI visibility is not a replacement for SEO, and it is not a separate discipline. It is a consequence of your content program. AI prompt testing is a low-cost way to test that program.
How to run the test: Simple prompts to try today
The goal is to cover the range of ways a real buyer might encounter your company through an AI interface. Run all five of these query types across at least two models. To do this, we recommend Perplexity (search-augmented) and ChatGPT without browsing (pure LLM). Comparing those results will give you a fairly accurate understanding of your brand’s AI footprint.
1: Category ranking prompt
“Who are the top 5 {your category} companies in {your target territory}?”
Start here. This is the cold-start query, because it has no brand name or prior context. If you are not on this list, a significant portion of your potential buyers may never reach your website at all, regardless of how well your other content performs. Run it with and without the territory qualifier, since some buyers will include it and some will not.
2: Category prompt
“What companies help {your target segment} with {the problem you solve}?”
This tells you whether you appear in your own category at all, and who you are being grouped with.
3: Comparison prompt
“How does {your company} compare to {your main competitor}?”
This reveals how your positioning and differentiation are understood (or not).
4: Problem-aware prompt
“We are a {company type} struggling with {core pain point}. What vendors should we consider?”
This tests whether you surface in intent-driven searches, which are closest to actual buying behaviour.
5: Branded prompt
“{Your company name}: what does it do, who is it for, and what do customers say about it?”
This is the direct lookup. If the answer is thin or wrong here, you have a foundational visibility problem.
Use specificity as a diagnostic tool
Once you have your baseline results, start narrowing the queries. Add a vertical or a key use case. You can also throw in geography. The point at which you begin appearing is a proxy for where your content has built a signal.
A practical example: a company that does not appear in “top ERP implementors in Israel” may appear clearly in “best ERP implementor in Israel for logistics companies” if they have a strong case study in that vertical. The case study has done its job. The AI can retrieve and use it for a specific query, even when the broader category query fails.
This is one of the most useful things AI prompt testing can tell you: a readout of whether your existing content is actually working.
- If you only appear when the query is highly specific, you have depth but not breadth (strong vertical proof but insufficient category authority).
- But if you appear in broad queries and disappear when a vertical or use case qualifier is added, you have awareness but no evidence.
Both diagnoses point to different investments.
The corollary is also worth noting: the more specific your content assets, the more likely they are to drive visibility for the qualified queries that actually convert. For example: a generic capabilities page competes with every other vendor in your category. But a case study about a logistics company doing ERP consolidation competes with almost no one.
Quick note on geolocation
If your target markets are in Israel and the US, the country you run the test from can affect your results, but the extent depends on the model.
- Search-augmented tools like Perplexity pull live results from localised search indices, so a query run from Tel Aviv may surface different source material than the same query run from New York. For these tools, running the test from within each target market (or using a VPN set to that region) gives you a more accurate picture of what buyers there would see.
- Pure LLMs without live search (Claude, base ChatGPT without browsing) generate responses from a fixed training corpus and are not meaningfully affected by the user’s location.
As a rule, run the test on both types of model. The gap between their results is itself informative: it tells you how much your AI presence depends on live web content versus trained knowledge. Also worth testing: if your Israeli buyers prompt in Hebrew rather than English, run the same queries in both languages. Hebrew-language corpora are typically thinner, and the results can differ substantially.
Reading what comes back
Evaluate each result across five dimensions.
- Presence: Are you mentioned at all? In category and problem-aware queries especially, absence is the worst outcome. It means you do not exist in the AI’s model of your market.
- Accuracy: Is the information current and factually correct? Outdated product descriptions, wrong founding dates, and deprecated use cases all erode trust before a conversation begins.
- Framing: Is your company described the way you would describe it? Pay attention to the language used. Ideally, you want it to reflect your ICP, your positioning, and the problem you prioritize. What you don’t want is generic vendor language.
- Competitive context: Who are you being grouped with? If you are consistently named alongside competitors you do not consider direct rivals, or absent from shortlists where you should appear, your category positioning needs work.
- Proof point density: Does the response cite customer outcomes, specific use cases, or named verticals? Or is it all capability language with no evidence? AI models synthesize what they find. Thin proof points in the output usually mean thin proof points in your content.
AI prompt testing results that should concern you
There are, unfortunately, many ways to lose the AI visibility race. These are the most urgent ones to expose and remediate before they start to deteriorate your pipeline.
- Ghost: Your company does not appear. Category and problem-aware queries return competitors only. This is the most common result for early-stage and under-invested brands, and the most urgent to fix.
- Commodity: You are present but described in entirely generic terms. “A software platform that helps businesses manage X” has no differentiation, and offers no evidence. The reader has no reason to choose you over anyone else.
- Outdated: The AI describes a version of your company that no longer exists. Old product names, previous positioning, or a use case you have moved away from. This happens when your older content has more authority and reach than your newer content.
- Misframed: You appear, but the description positions you incorrectly. Wrong segment, wrong pain point, wrong buyer. This can actively harm pipeline by attracting the wrong inbound or creating confusion in a sales conversation.
- Competitor-owned: The category query that should return you as a primary answer instead returns a rival. They own the framing. You are a footnote, or absent entirely.
Why AI models describe your company the way they do
LLMs synthesize their answers from patterns in the content that trained them. In search-augmented tools, they pull from the live web results retrieved at query time. Understanding which signals carry weight is the starting point for improving your results.
Third-party citation
Content that references your company from external, authoritative sources carries more weight than your own website. Press coverage, analyst mentions, customer case studies published on third-party platforms, and integration partner pages all contribute.
Structured and consistent claims
Models favour content where the same core claims — your category, your ICP, your primary differentiator — appear consistently across multiple sources. If your website says one thing, your G2 profile says another, and your LinkedIn says a third, the output will be vague or contradictory.
Recency matters
Content that has been published or updated recently tends to have more weight in search-augmented models. Old product pages and stale case studies or blogs drag your AI presence toward an earlier, less accurate version of your company.
Your results weren’t what you wanted. Here’s what to do next.
Start by identifying which of the five failure patterns applies to the results of your AI prompt testing experiment. Each points to a different root cause and a different fix.
Invisible? Get specific
For ghosts, the priority is building a base of authoritative, indexable content that establishes your category and ICP clearly. A pillar content programme is the most reliable way to become visible. It should cover long-form articles, structured use case pages, and earned coverage.
Commodity-type responses need more differentiation
If you are surfacing like a commodity, the problem is differentiation at the content level. Your claims are present but not distinctive. Invest in customer proof: case studies with specific outcomes and vertical-specific content work well here.
Being out of date is a broad content problem
Brands whose AI footprint is out of date should invest in an audit and systematic refresh. Identify the assets that are driving your current AI presence and bring them into alignment with your current positioning. New content alone will not displace old content that has accumulated authority.
Competitors are eating your lunch
The issue here is usually a combination of positioning clarity and content volume. Your competitor is being returned because they have more content, more citations, and more consistent language around the category you both serve. Closing that gap takes a sustained programme, not a single campaign.
Let’s run the test, then build the solution: AI search visibility with Inspired Marketing
Before the week is out, run these five prompts on two models. Screenshot the results. If what comes back concerns you, that is a content and positioning problem with a clear solution.
It is also a problem that compounds over time. The companies investing in AI visibility now are building a structural advantage in how they are perceived before a sales conversation even starts.
If you want a second opinion on what your results mean and what to prioritise, that is a conversation we are happy to have.
