Your next customer is already interviewing vendors. They just aren't doing it on your website. They're typing questions into ChatGPT, Perplexity, Gemini, Claude and Copilot, and the AI is quietly assembling a shortlist of tools to recommend before a single tab opens on your domain. This is the pre-website funnel, and the prompts buyers type are the raw material that decides who makes the AI shortlist and who never gets mentioned.
TL;DR: B2B buyers now run 5-15 buying-intent prompts through AI assistants before they visit any vendor site. This library catalogs 120+ of the real prompts across 10 categories. Map them to your product, check whether AI recommends you for each, and close the gaps where a competitor gets named and you don't.
To get value from this page:
- Pick the categories that match how your product is bought. Most B2B SaaS teams need all ten.
- Replace the placeholders, such as [category], [competitor] and [use case], with your real category, competitors and use cases.
- Run each prompt in ChatGPT, Perplexity, Gemini, Claude and Copilot, logged out or in a temporary chat.
- Record who gets named. Every prompt where a competitor appears and you do not is a gap to close.
What "buying-intent prompts" actually are
A buying-intent prompt is any question a prospective buyer asks an AI assistant to evaluate, compare, shortlist or choose between software products during an active purchase. It is the AI-native equivalent of a bottom-of-funnel search query, except the answer arrives as a recommendation instead of a list of blue links. Visibility gets you seen. Recommendations get you chosen. And these prompts are where the choosing happens.
The shift matters because the moment of influence has moved earlier. In the old funnel, you competed on your landing page. In the AI-assisted buying journey, the AI has already narrowed the field to three or four names before the buyer clicks anything. If you want to understand the mechanics behind that shift, our breakdown of how AI search engines actually work is the technical foundation. The prompts below are the demand signals flowing through that machine every day.
Recommendation Frequency, how often AI names you when a buyer asks a buying-intent prompt in your category, is the single metric that predicts whether you win the pre-website funnel.
This is exactly the problem Arobis AI solves for B2B SaaS. The job isn't to log each time you get mentioned. It's engineering the authority that makes AI recommend you when these prompts get asked. Now, the library.
1. Discovery prompts, the "best [X]" openers
These are how buyers start when they know the problem but not the vendors. Getting named here seeds the entire shortlist, because the first three tools an AI lists become the default frame for every follow-up question. If you're absent from these answers, the cause is usually structural, not creative. When a discovery prompt skips you, start with the 5 signals AI engines use to decide which brands to recommend. In buying-intent research the usual cause is one of three: too few outside sources vouching for you, a fuzzy answer to "what category is this," or pages a model struggles to read.
- What are the best [category] tools for B2B SaaS companies?
- What is the best [category] software for a 50-person company?
- Which [category] platforms do fast-growing startups use?
- What are the top [category] tools right now?
- Recommend a [category] tool for a team that is just getting started.
- What [category] software do most mid-market SaaS companies use?
- Give me a shortlist of [category] vendors worth evaluating.
- What tools help a B2B team solve [problem]?
- What is the easiest [category] tool to set up?
- Which [category] tools are best for enterprise teams?
- What are the leading [category] companies I should know about?
- Which [category] tools are worth paying for?
2. Comparison prompts, "[X] vs [Y]"
Comparison prompts fire when the buyer has two or three candidates and wants a tiebreaker. These are high-intent and often the last question asked before a demo request, which makes them disproportionately valuable.
- [Product A] vs [Product B]: which is better for a B2B SaaS team?
- How does [Product A] compare to [Product B] on pricing?
- What are the main differences between [Product A] and [Product B]?
- Is [Product A] or [Product B] better for a small team?
- Compare [Product A], [Product B] and [Product C] for [use case].
- Which is easier to use, [Product A] or [Product B]?
- Which has better integrations, [Product A] or [Product B]?
- [Product A] or [Product B] for an enterprise rollout?
- Which has better customer support, [Product A] or [Product B]?
- What do users prefer about [Product A] over [Product B]?
- Is [Product A] worth the extra cost over [Product B]?
- Which scales better as a company grows, [Product A] or [Product B]?
For the comparison prompts in this library, ChatGPT tends to repeat claims it can copy word for word, so structure and specifics decide who wins; how to rank in ChatGPT shows the patterns that work.
3. Alternatives prompts, "alternatives to [X]"
Alternatives prompts are pure switching intent. The buyer already uses or considered a competitor and is looking for a reason to leave. If your competitor is the anchor and you're not in the answer, you're losing deals you never see.
- What are the best alternatives to [competitor]?
- What is a cheaper alternative to [competitor]?
- What should I switch to if [competitor] is too expensive?
- What are open-source alternatives to [competitor]?
- Which [category] tools are better than [competitor] for small teams?
- What do companies use instead of [competitor]?
- What is the best [competitor] alternative for enterprise?
- Is there a simpler alternative to [competitor]?
- Which [competitor] competitors have better support?
- Which [competitor] alternatives have a free plan?
- What are alternatives to [competitor] that integrate with [tool]?
- Why are teams moving away from [competitor], and what are they switching to?
Perplexity is where alternatives prompts get researched most aggressively because it surfaces sources inline; earning those citations is the subject of our playbook on how to rank in Perplexity.
4. Use-case and jobs-to-be-done prompts
These prompts describe a job the buyer needs done, not a category they're shopping. They matter because the AI has to translate the job into a category and then a vendor, and if your positioning maps cleanly to the job, you get recommended even by name-blind buyers.
- What tool can help me [job to be done] without hiring more people?
- How can a B2B SaaS team automate [task]?
- What is the best way to [job to be done] for a remote team?
- Which software helps reduce [problem] in [department]?
- How do growing companies handle [process] at scale?
- What tool should I use to track [metric] across teams?
- How can I [job to be done] without writing code?
- What do RevOps teams use to [job to be done]?
- Which platform helps [role] save time on [task]?
- What is the fastest way to set up [process] for a new team?
- How do I replace spreadsheets for [process]?
- What software helps a small team [job to be done] like an enterprise does?
Ranking for jobs-to-be-done language is the heart of answer engine optimization, because you're optimizing for the question behind the query, not the keyword. This is also where Answer Optimization inside the Arobis AI framework does most of its work.
5. Budget and pricing prompts
Pricing prompts appear once a buyer is serious enough to model cost. AI assistants answer them constantly, often with outdated or wrong numbers, which means clear, current, machine-readable pricing content is a competitive edge.
- How much does [product] cost per user?
- What is the cheapest [category] tool that is still good?
- Is [product] worth the price for a startup?
- What does a typical [category] tool cost for a 100-person company?
- Which [category] tools have a free plan?
- Does [product] offer a discount for annual billing?
- What are the hidden costs of [product]?
- Which [category] tools fit a budget under $500 a month?
- How does [product] pricing compare to [competitor]?
- Is there a free trial for [product]?
- What is the total cost of ownership for [category] software?
- Which [category] vendor offers the best value for money?
When a buyer asks these, the AI frequently pulls straight from your pricing page, which is why keeping clear, current pricing that models can read matters as much as the number itself. Vague or gated pricing gets skipped.
6. Integration and tech-fit prompts
Integration prompts are disqualifiers in disguise. A buyer with a fixed stack won't consider a tool that can't connect, so being named here is often binary: you're in the consideration set or you're eliminated silently.
- Does [product] integrate with Salesforce?
- Which [category] tools integrate with HubSpot?
- What [category] software works with Slack and Microsoft Teams?
- Does [product] have an open API?
- Which [category] tools connect natively to [data warehouse]?
- Can [product] sync with our existing [tool]?
- Which [category] platforms support SSO and SCIM?
- Does [product] work with Google Workspace?
- Which [category] tools have a Zapier integration?
- What [category] software fits a Microsoft-based stack?
- How hard is it to migrate our data from [competitor] to [product]?
- Does [product] support webhooks?
These prompts are also where product and integration pages earn their keep, because the models weight structured feature content heavily. That is the reasoning behind why AI search engines prefer your product and landing pages. Copilot in particular, sitting inside the Microsoft stack, leans hard on integration signals; see how to rank in Microsoft Copilot.
7. Trust, reviews and social-proof prompts
Trust prompts are the buyer sanity-checking a candidate before advancing it. The AI answers by synthesizing reviews, forums, case studies and third-party mentions, so your Recommendation Frequency here depends on off-site authority far more than on your own copy.
- What do customers say about [product]?
- Is [product] reliable?
- What are the most common complaints about [product]?
- Is [product] legit?
- What are the pros and cons of [product]?
- Which [category] tools have the best reviews on G2?
- Do real users recommend [product]?
- What does Reddit say about [product]?
- Which companies use [product]?
- Is [vendor] a stable company to buy from?
- How good is [product] customer support?
- Are there case studies showing results with [product]?
Community signal, especially Reddit, carries outsized weight in how models judge trust, which is why we wrote a dedicated Reddit strategy guide for SaaS. This is Authority Engineering in practice: building the third-party evidence AI reads before it vouches for you.
8. Objection and risk prompts
Objection prompts surface the buyer's fear. When the AI answers "is [product] hard to implement?" its response can kill or save a deal, and the answer is built from whatever evidence exists, not from your sales team's rebuttals.
- Is [product] hard to implement?
- How long does it take to roll out [product]?
- What are the risks of switching to [product]?
- Is [product] secure enough for enterprise data?
- Is [product] SOC 2 compliant?
- What happens to our data if we cancel [product]?
- Does [product] lock you into a long contract?
- What are the downsides of [product]?
- Will [product] work for a team our size?
- How steep is the learning curve for [product]?
- Is [product] GDPR compliant?
- What problems do teams run into after buying [product]?
Neutralizing objection prompts requires content the models can quote against the fear directly. Understanding what AI crawlers actually read tells you which pages and formats get ingested and which get ignored when the AI assembles its risk assessment.
9. Industry and vertical-specific prompts
Vertical prompts narrow the field by context: same category, different requirements. If your positioning claims a vertical explicitly, you win these even against larger horizontal competitors, because the AI rewards specificity.
- What is the best [category] software for healthcare companies?
- Which [category] tools are built for fintech?
- Which [category] platform works best for agencies?
- What is the best [category] tool for manufacturing companies?
- Which [category] software is HIPAA compliant?
- What [category] tools do ecommerce brands use?
- What is the best [category] platform for professional services firms?
- Which [category] tools are popular with marketplaces?
- What [category] software suits regulated industries?
- What is the best [category] tool for education companies?
- Which [category] platforms do logistics companies use?
- What [category] tool works best for B2B SaaS specifically?
Google's surfaces are where vertical intent concentrates, and being pulled into an AI Overview for "[category] for [industry]" pays off out of proportion to the effort. Our guides on ranking in Google AI Overviews and ranking in Google Gemini cover both entry points.
10. Buying-stage and decision prompts
These are the closing questions, the buyer asking the AI to make the call. They fire late, they carry the most intent, and the answer often becomes the final shortlist the buyer acts on. Own these and you own Demand Capture.
- Which [category] tool should I choose for my team?
- I am deciding between [Product A] and [Product B]. Which one should I buy?
- For a team of [size] with a budget of [amount], which [category] tool should we pick?
- What is the safest choice for a [category] platform?
- Which [category] vendor would you recommend for a multi-year contract?
- Give me a final recommendation for [use case].
- Which of these is the best fit for us: [Product A], [Product B] or [Product C]?
- What questions should I ask [vendor] before signing?
- Should we buy [category] software now or wait?
- Rank these [category] tools for a company like ours.
- Which [category] tool would you choose if you were the head of [department]?
- Which [category] tool is most likely to be adopted by the whole team?
Decision prompts are increasingly answered by Claude in longer, reasoned buying analyses, so being present in that reasoning matters; see how to rank in Claude. The broader discipline of shaping generated answers across every model is generative engine optimization.
How to turn this library into pipeline
A prompt library is only useful if you act on it. The workflow below turns these questions into Recommendation Frequency, mapped to The Arobis AI Search Demand Framework™: Discoverability, Recognition, Authority, Recommendation, Demand Capture.
- Discoverability. Pick 20 to 30 prompts from this library and localize them to your category. Confirm AI crawlers can reach the pages that should answer them.
- Recognition. Run every prompt across the five assistants and record whether you are named, in what position, and how you are described.
- Authority. For each prompt a competitor wins, list the sources the engines cite for them. Earn a place on those pages: reviews, comparisons, community threads and expert roundups.
- Recommendation. Publish answer-ready pages for the prompt categories you lose most, with clear claims, current pricing and fair comparisons the models can quote.
- Demand Capture. Re-run the same prompts on a fixed schedule, track Recommendation Frequency, and connect AI referral traffic to pipeline in your analytics.
Doing this manually across dozens of prompts and several models is slow. An AI Visibility Audit does it at scale, scoring your Recommendation Frequency across the prompts that matter, with the report back within 24 hours. Then read the State of AI Search Visibility report for the benchmarks that tell you whether your numbers are competitive.
Why this is the new demand channel, not a side project
The prompts in this library represent demand that exists whether or not you show up for it. Every day, buyers in your $1M-$20M ARR category are building their AI shortlist inside these tools, and the vendors named in those answers capture intent the rest never see. This is the pre-website funnel, and it compounds: the more often AI recommends you, the more it treats you as the default, which drives more recommendations.
These prompts are also ad inventory now. ChatGPT matches ads to the conversation, so a competitor can buy the space under your best buying question. Our guide to ChatGPT ads for B2B SaaS covers how that works and what it costs.
That's the difference between monitoring and demand generation. Knowing you're mentioned 12% of the time is a metric. Engineering the authority that moves it to 60% is AI Search Demand Generation, the category Arobis AI was built to own. Being seen is a vanity number; being picked is the one that closes revenue.
A prompt library is only half the job. The other half is measuring ChatGPT visibility properly once you start running these prompts, and using AI brand monitoring to track what the answers say about you over time.
Frequently asked questions
How many prompts do B2B buyers actually run before choosing SaaS?
In practice, most run somewhere between 5 and 15 buying-intent prompts across a purchase, clustering around discovery early and comparison, pricing and decision prompts near the end. The exact count varies by deal size, but the pattern is consistent: the AI shortlist is largely set before the buyer visits any vendor site, which is why the prompts in this library are worth engineering for.
How do I know which prompts my buyers are asking?
Begin with the categories that match how your product is bought, then localize the placeholders with your real category, competitors, use cases and verticals. For a quick read on whether buyer prompts already return your name or a competitor, our ChatGPT visibility checker runs five buyer prompts for you, which beats guessing prompt by prompt.
Is optimizing for AI prompts different from traditional SEO?
Yes. Traditional SEO competes for a ranked list of links a human scans; AI search competes to be the named recommendation inside a synthesized answer. The signals overlap but the outputs differ, because you're optimizing for extraction, citation and recommendation, not click-through position. The mechanics of AI search retrieval explain why the same page can rank on Google yet never get recommended by ChatGPT.
Which AI assistant should I prioritize?
Prioritize where your buyers concentrate, but assume they use several. ChatGPT dominates general discovery, Perplexity leads research-heavy comparison and alternatives prompts, Gemini and AI Overviews own Google-native intent, Copilot sits inside the Microsoft workday, and Claude increasingly handles longer reasoned decision prompts. Coverage across all five is what protects your Recommendation Frequency.
What if AI recommends a competitor instead of us?
That's a recognition-and-authority gap, and it's fixable. The AI is naming the vendor with clearer positioning and stronger third-party evidence for that specific prompt. Closing it means fixing how you're framed and building the authority signals models read, the work Arobis AI does, priced transparently on our plans and pricing page.
How often should I re-check these prompts?
Quarterly at minimum, monthly if your category is competitive, because model outputs shift as content, reviews and competitors change. Treat Recommendation Frequency like a pipeline metric you monitor, not a one-time audit. Benchmark yourself against the wider market using our research on AI search visibility so you can tell if you're gaining or quietly losing ground.
Ready to see where you stand? Check how ready your site is for AI answers with our free checker, then talk to us about getting recommended inside AI answers for the buying-intent prompts that decide your category.
Related guides
- 11 Best Free AI Visibility Tools to run these buying-intent prompts and check your Recommendation Frequency at no cost
- Best Peec AI alternatives, compared on price and prompt coverage for tracking these prompts at scale
- Best Otterly AI alternatives, for teams that need more than one engine tracked per prompt
