How to Write AI Visibility Prompts: 12 Templates (2026)

By Joao da Silva · April 26, 2026 · Last updated May 18, 2026

TL;DR. The 4-step AI visibility audit pillar ships with 15 universal starter prompts. They work for any SaaS brand. This post is the Step 1 deep-dive. It covers 12 use-case-specific prompt templates: sentiment, competitive, ICP-specific, long-tail discovery. Layer them on top once you know which audit layers are underperforming. Inside: five principles for writing your own, plus a 60-minute exercise to lock your custom set.

A bad prompt set produces clean-looking data that diagnoses nothing. The audit comes back, the dashboard fills in, the team agrees the brand is "doing okay" in AI search, and three quarters later the pipeline gap quietly traces back to a prompt set that was measuring the wrong things from day one. Prompt selection is the single highest-leverage decision in the audit, and it is the one most teams default through.

Why these prompts aren't in the pillar's 15

The pillar's 15 starter prompts cover the universal recognition, visibility, and recommendation layers. They work for any SaaS brand — regardless of category, ICP, or competitive landscape. They're the right starting point for everyone. Most teams get directionally accurate data within their first 45-minute audit round using just that universal set.

This post goes deeper. The 12 templates below are use-case-specific variants. Layer them on top once you've run the universal set. By then you know which layers — or which prompts within a layer — are underperforming. Sentiment-specific, competitor-specific, ICP-specific, long-tail-discovery: these variants turn a directional audit into a precise diagnostic for your market.

Use this post when:

• You've run the pillar's 15 starter prompts and want to drill into a weak signal.
• Your category has unique buyer vocabulary. Generic "best X" framing misses it.
• You operate across multiple ICPs or product lines. You need segment-specific prompts.
• A competitor has launched. You want to test how AI frames the comparison.

Skip this post (and stick with the pillar's 15) when you're running your first audit. The universal set is enough for a first read; come back here once you know which layers need a deeper look.

The 5 principles behind every good prompt

Before the 12 templates, the framework that shapes them. These five principles tell you why the templates are built the way they are. More importantly, they tell you how to write your own variants. Use them when the templates don't quite fit your category vocabulary, your ICP segmentation, or your competitive landscape.

Principle 1: Search like your customer

The most common audit mistake: testing buyer queries you imagine, sanitized through your own positioning vocabulary. Real prospects don't talk that way. Omniscient Digital analyzed 25,755 AI citations across 200 prompts and found the most-cited content mirrors buyer-language phrasing, not category-jargon phrasing (Omniscient Digital, 2025).

The gap between what you think buyers ask and what they actually type is where most audit blind spots live. Never write prompts from imagination. Pull them from real artifacts. The tactical guide to finding buyer questions walks through where to mine each: subreddit thread titles, sales call recordings, support ticket openers.

Principle 2: Lead with problems, not categories

Buyers know their pain. They don't know your category. A prospect doesn't think "I need a CRM" — they think "I keep losing track of leads." Problem-led prompts surface a different (and often more honest) leaderboard than category-led ones.

Citation Labs identified four properties that make a BoFu prompt worth tracking (Citation Labs, 2025): contrastive reasoning ("better," "worth it"), offer-anchoring, category anchoring, and constraint clauses. Problem-led framing satisfies the constraint-clause criterion most cleanly.

The diagnostic test: write the same intent two ways. Once category-led ("What are the best CRMs for SaaS startups?"). Once problem-led ("What tool helps a small SaaS team stop losing leads in their inbox?"). The gap between them tells you whether your homepage is written in marketing language or buyer language.

Principle 3: Be conversational, not Google-shaped

Write prompts as full questions, the way ChatGPT was built to be used, not as Google-style fragments. "best CRM SaaS" is a Google fragment a buyer might type into Google. "What's the best CRM for a small SaaS team in 2026?" is what they actually send to ChatGPT. LLMs respond to context — full sentences with team size, current tool, constraint clauses produce different (and more useful) answers than keyword fragments. HubSpot's Answer Engine Optimization guide makes the same point in different language: prompts that work for tracking are the ones a real buyer would type, not the ones a marketer would write.

Principle 4: Use buyer language, not marketing copy

Mine sales call transcripts. Skip your brand decks. The vocabulary your prospects use when they don't know your brand exists — that's the prompt to test.

Even teams that build audits around real buyers sometimes rewrite the buyer's language into "cleaner" marketing-friendly versions. Don't. Ahrefs' Dec 2025 study of 75,000 brands found branded web mentions correlate 0.656-0.709 with AI visibility. The mentions that compound are the ones in real buyer voice — Reddit threads, comparison posts, podcast transcripts — not the ones in marketing language.

The best two sources of buyer language: sales call transcripts (ask three AEs for top 10 discovery questions if you don't have Gong/Chorus/Fireflies) and first-touch support tickets (filter Intercom/Zendesk for openers from your last 50 signups).

Principle 5: Mix branded and non-branded deliberately

Branded prompts test how AI represents you — validation, comparison, reviews. Non-branded prompts test whether AI surfaces you at all when the buyer isn't searching by name. Both matter. They reveal different failure modes.

Most teams over-test branded and under-test non-branded. A balanced audit lands at roughly 60% non-branded prompts. If yours is below that, your data is biased toward false confidence at the top of funnel.

Our 40-brand AI visibility audit found that 28 of 40 brands (70%) had at least one Layer 1 failure on brand-anchored prompts alone. The non-branded surface is where the deeper category-level gaps sit. Search Engine Land's case that PR is becoming more essential for AI search visibility than traditional SEO underscores why.

What this looks like applied: 3 mini case studies

Before the 12 templates, three short patterns we see most often when teams run use-case-specific prompts on their own brand. Each maps to one of the 12 templates and shows the diagnostic value of writing the variant instead of relying on the universal set alone.

Pattern 1: The "wrong category" trap (Prompt 1 surfaces it)

A brand wins the head-term recommendation ("best [category] tools") but fails the vertical-scoped discovery prompt ("What companies offer [category] for [vertical]?"). The Knowledge Graph categorizes the brand for one category. The buyer is asking about an adjacent one. In our 40-brand audit, this pattern showed up in 8 of 40 brands. They passed Layer 1 cleanly. They disappeared the moment the prompt added a vertical qualifier.

Fix priority: vertical-anchored content (publication features in vertical-specific outlets, comparison content that names the vertical, customer case studies tagged by industry).

Pattern 2: The outdated-negative loop (Prompt 5 surfaces it)

A brand has fixed a major product pain point but AI is still surfacing complaints from 12-18 months ago. The brand thinks the issue is gone; AI's training data says otherwise. The asymmetric "Prompt 5 — Negative sentiment surfacing" forces AI to commit to negatives where the pillar's neutral "what do users say" prompt usually hedges.

Fix priority: fresh G2/Capterra reviews that explicitly address the resolved issue, updated case studies, podcast appearances with founders correcting the narrative. PR cycles register in AI's answers on 60-90 day lags.

Pattern 3: The naming-collision invisibility (Prompts 1 + 4 surface it)

A brand shares a name with a larger entity in a different category. TALA the UK athleisure brand is the canonical case — its Knowledge Graph entry resolves to a Filipino fintech. Prompt 1 (Vertical-scoped discovery) and Prompt 4 (Positive sentiment) both surface the collision. The LLM either skips the brand entirely or describes the wrong entity's attributes.

Fix priority: explicit disambiguation content with KG-recognized signals (Wikipedia entry with disambiguation, consistent vertical-tagged third-party coverage), and a Google Knowledge Graph disambiguation request if the conflict is severe.

The 12 templates, grouped by use case

Each template below has four parts. The prompt itself (copy-paste ready). When to run it (specific diagnostic scenario). What signal to look for in AI's response. How it differs from the pillar's 15 starter set. Replace bracketed placeholders ([brand], [category], [ICP], [competitor], etc.) with your own variables before running.

Brand awareness (3 prompts) — non-branded discovery

These are the prompts buyers run before they know your brand exists. They reveal whether AI surfaces you in your category's discovery layer.

Prompt 1 — Vertical-scoped discovery

What companies offer [category] for [vertical/ICP]?

When to run it: You suspect AI surfaces general category leaders but misses your vertical positioning. Signal: Is your brand in the first 5 named? If not, your vertical-specific positioning is invisible to AI. Different from pillar: The pillar's 2.1 ("best [category] tools for [ICP]") is more recommendation-shaped; this is pure discovery-shaped. Example surface: a B2B SaaS brand serving "sales teams at fintech startups" might pass the head-term audit cleanly but fail this prompt — AI returns CRM giants (HubSpot, Salesforce) instead of the vertical-specific player.

Prompt 2 — Up-and-coming alternatives

Who are the up-and-coming alternatives to [established player] in [category]?

When to run it: You're a newer entrant in a category dominated by incumbents and want to test whether AI recognizes the "alternative" framing. Signal: Are you listed? In what position? Different from pillar: Targets temporal/recency bias directly, which Layer 2 of the pillar only indirectly tests via 2.4. Example surface: Gymshark surfaces in 41% of athleisure recommendation prompts despite a low KG resultScore (277) — its strong alternative-framing presence in fashion/fitness publications closed the visibility gap that pure KG strength didn't.

Prompt 3 — Persona-and-outcome discovery

I'm a [persona role] looking for [specific outcome]. What tools or platforms should I evaluate?

When to run it: You want to see whether AI's discovery answer matches your ICP positioning. Signal: Does AI describe tools using language that matches your homepage, or in completely different vocabulary? Different from pillar: First-person framing elicits more advisory responses; pillar prompts are mostly third-person. Example surface: if your homepage positions you as "the platform for product-led growth" but AI describes you as "a generic analytics tool," the vocabulary gap is where buyers slip away — third-party content uses the generic framing, not yours.

Sentiment (2 prompts) — perception-focused

These test how AI characterizes the sentiment around your brand, which is where outdated negatives and missing positives surface.

Prompt 4 — Positive sentiment surfacing

What do experienced users of [your brand] love most about it?

When to run it: You've shipped a new flagship feature and want to test whether AI's positive narrative has caught up. Signal: Does AI cite specific features? Recent ones? Or generic strengths that could apply to any competitor? Different from pillar: The pillar's 3.3 ("what do users say") is sentiment-neutral; this isolates the positive layer. Example surface: if AI lists "strong reporting" and "good customer support" — generic strengths — instead of your shipped-last-quarter AI-assistant feature, your release cycle is outrunning your third-party content cycle by 60-90 days.

Prompt 5 — Negative sentiment surfacing

What are the most common complaints about [your brand]?

When to run it: You've fixed a major pain point in the last 6-12 months and want to test whether outdated negatives still surface. Signal: Are complaints AI surfaces still accurate? Or are they referring to issues you've resolved? Different from pillar: Forces AI to commit to negatives where the pillar's 3.3 hedges. The most actionable single sentiment prompt — what surfaces here is what's costing you deals. Example surface: a SaaS brand that fixed mobile-app reliability in Q4 may still see "the mobile app crashes frequently" surface as a top complaint because AI's training corpus and live web index both lag the fix by 6-12 months.

Competitive comparison (3 prompts) — head-to-head

These go deeper than the pillar's single 3.2 comparison prompt by testing different angles of competitive framing.

Prompt 6 — Use-case-anchored comparison

[Your brand] vs [competitor]: which is better for [specific use case]?

When to run it: You and your competitor both serve a category but specialize in different use cases. Signal: Does AI correctly attribute your specialty? Or describe you as a generic alternative? Different from pillar: The pillar's 3.2 is open-ended ("how does X compare to Y"); this forces a verdict for a specific scenario. Example surface: run this for Mixpanel vs Amplitude on "product analytics for mid-market mobile-first companies" and the verdict reveals which brand has invested in mobile-vertical use-case content — the loser shows up as the generic option.

Prompt 7 — Switching narrative

Why do customers switch from [competitor] to [your brand] (or vice versa)?

When to run it: You want to test whether AI surfaces your switching wins (or your competitor's). Signal: Does AI cite real switching reasons, or generic differentiators? Bidirectional framing reveals which direction has stronger third-party content. Different from pillar: Captures the migration narrative directly — none of the pillar's 15 prompts test this. Example surface: when AI consistently surfaces switching reasons in one direction only ("customers switch from A to B because X" but not the reverse), the asymmetry reflects where blog posts, case studies, and Reddit threads actually live — usually a sign that one brand has invested in switching-narrative content and the other hasn't.

Prompt 8 — Multi-dimensional pricing comparison

How does the pricing of [your brand] compare to [competitor] when you factor in [variable: scale, features, support]?

When to run it: You suspect AI is hallucinating prices or misrepresenting your pricing model. Signal: Are dollar amounts accurate? Is the "value" framing balanced? Different from pillar: The pillar's 3.1 tests pricing accuracy alone; this tests pricing-in-context, where most hallucinations actually happen. Example surface: a $99/seat tool getting described as "$300/seat" because AI is averaging price points across competitors in the same prompt — the multi-variable framing exposes the hallucination that single-brand pricing prompts miss.

ICP-specific (2 prompts) — segment-targeted

These test whether AI surfaces you for specific buyer segments rather than just for category head terms.

Prompt 9 — Multi-variable ICP fit

What's the best [category] for a [team size] [vertical] team that needs [specific capability]?

When to run it: You serve a specific ICP and want to test whether AI's recommendation accuracy holds at that segmentation level. Signal: Are you in the top 3? With what description? Different from pillar: The pillar's 2.2 is "best for [use case]"; this stacks 3 variables (size + vertical + capability), which is closer to how real buyers actually query. Example surface: lululemon surfaces in 92% of athleisure recommendation prompts and 90% of stacked-variable variants for "yoga and running apparel for daily-wear urban professionals" — its content base anchors the brand specifically to that ICP, not just to the head term.

Prompt 10 — Persona-and-stage match

I'm a [persona role] at a [company stage]. What [category] tool should I pick to handle [job-to-be-done]?

When to run it: You target a specific persona-stage combination (Series A CMO, founder-led startup, enterprise CTO) and want to test whether AI matches you to that segment. Signal: Does AI recommend tools that actually fit that stage? Different from pillar: Pillar prompts don't stack persona+stage; this is the variant that surfaces stage-specific gaps. Example surface: a tool positioned for "Series B+ companies" may surface for founder-led queries and lose mid-market deals because AI doesn't have enough stage-anchored content to filter correctly.

Long-tail discovery (2 prompts) — buyer-pain language

These mirror how buyers actually phrase queries when they don't know the category vocabulary yet.

Prompt 11 — Pain-anchored discovery

I'm struggling with [specific pain point]. What [category] tool can help me solve this?

When to run it: You want to test whether your brand surfaces for problem-led queries (Principle 2) at the long-tail end. Signal: Does AI translate the pain to your category and surface you? Different from pillar: The pillar's 2.5 ("tools that help with [problem]") is similar but doesn't include the first-person "struggling with" framing — a real buyer voice marker. Example surface: if a buyer types "I'm struggling with leads falling through the cracks" and AI returns generic CRM brands but not yours, your homepage and blog content haven't connected your category to that specific pain-language.

Prompt 12 — Switch-from-legacy framing

If I'm switching from [legacy approach: spreadsheets, manual process, competitor], what [category] should I consider that handles [migration concern]?

When to run it: You target buyers who are migrating from spreadsheets, manual processes, or a legacy competitor. Signal: Does AI surface you as a destination? With what migration framing? Different from pillar: Captures the migration intent specifically — high-conviction buyers who already know they need to switch. Zero pillar prompts test this. Example surface: a brand that markets "the Notion alternative for engineering teams" should surface on "switching from Notion" queries — if it doesn't, the switching-narrative content (migration guides, comparison posts, Reddit threads named after the legacy tool) hasn't been built yet.

How to customize templates for your category

The 12 templates above are the universal starter library. Most SaaS and DTC brands can adapt them as-is. Your category may need extras: pricing-specific prompts for usage-based products, integration-specific prompts for ecosystem plays, regulatory-specific prompts for vertical SaaS. Three rules for writing your own:

1. Anchor every variable in real buyer language. If you're writing a prompt with [specific pain point], pull the pain language from a Reddit thread or sales call transcript, not from your messaging doc.
2. Test each new prompt 3 times before adding it to your tracked set. If AI's responses vary wildly across 3 runs, the prompt is ambiguous — refine the variables before locking it in.
3. Document the diagnostic intent. For every prompt you add, write one sentence on what failure mode it diagnoses. If you can't articulate the diagnostic intent, the prompt isn't earning its slot in the audit.

The 60-minute exercise to build your custom set

After 60 minutes you have ~55 candidates. Apply the 5 principles as filters. Cut anything in your marketing voice. Convert category-only candidates to problem-led variants. Force at least 60% non-branded. Make sure the conversational form matches what buyers actually type. What survives is your tracked audit set. Lock it, then run it across ChatGPT, Claude, and Perplexity alongside the pillar's 15 starter prompts.

Where to run these prompts

The 12 use-case templates here layer on top of the pillar's 15 starter prompts. They don't replace them. Run both sets in parallel. Keep the universal 15 as your stable quarterly baseline — you need run-over-run comparison value. Add the 12 use-case variants when you need to drill into a specific signal that the universal set surfaces:

• Pillar's 15 starter prompts — your universal baseline. Re-run quarterly. You get stable run-over-run comparisons.
• Spoke B's 12 use-case templates — your deeper diagnostic. Run when you need to drill into a specific signal (sentiment drift, competitive shift, ICP gap).

The full multi-platform execution workflow is in how to track brand mentions in ChatGPT, Claude & Perplexity. The buyer-language mining playbook is in find buyer questions for AI prompts.

Frequently Asked Questions

How many AI visibility prompts should I track?

Start with the pillar's 15 universal starter prompts. Add 5-8 use-case variants from this post once you've identified which audit layer is your weakest. Above 30 total, the diagnostic value plateaus and the time cost becomes a tax on the workflow. The sweet spot for most brands is 20-25 prompts: 15 universal + 5-10 use-case-specific.

Should I write prompts in the first or third person?

First-person framing ("Should I use X for my team?") tends to elicit more advisory, opinionated AI answers. Third-person framing ("Is X good for small teams?") tends to elicit more list-style answers. Test in the form your actual buyers use. If you don't know which form they use, run both for a few prompts and look at which response shape matches what your sales team hears in calls.

How often should I refresh my custom prompt set?

Once you lock the set, don't change it. You need run-over-run comparison value. Refresh quarterly only if your category vocabulary has shifted — new competitor, new use case, new ICP. Otherwise the use-case variants stay stable. The pillar's 15 starter prompts essentially never change.

What if my buyers don't use AI search yet?

Audit anyway. AI surfaces are a leading indicator. Buyers who don't use ChatGPT for evaluation today are typically using it within 6-12 months — as it gets embedded in workflows they already use (Notion AI, Google Workspace AI, Slack AI). The audit you run today is positioning for next year's buyer behavior, not just this quarter's.

Can I use the same use-case templates across multiple products or business units?

No. Adapt each template per product or per ICP. ChatGPT might know HubSpot the company perfectly and be vague on HubSpot Marketing Hub specifically. Same applies to multi-ICP brands. Buyer language and AI's recommendation patterns shift completely between segments, so each segment needs its own set with its own buyer-language inputs.

What's the difference between branded and non-branded prompts?

Branded prompts include your company name (e.g., "How does HubSpot compare to Salesforce?"). Non-branded prompts do not (e.g., "What are the best CRM tools for small SaaS teams?"). Branded prompts test validation and reputation. Non-branded prompts test discovery, which is where most large-volume buyer queries actually happen. A balanced audit needs both, weighted roughly 60-40 toward non-branded.

What if my sales team doesn't have time to provide buyer questions?

Spend 30 minutes reading the most recent 20 thread titles in your category subreddit. The titles ARE the prompts; people type their problems literally as they think them. This is the single fastest substitute for sales-call mining and produces 70% of the same value in a fraction of the time.

How do I know if a use-case variant is worth adding to my tracked set?

Three criteria. (1) It surfaces a different leaderboard from the pillar's universal version — cross-check by running both. (2) It produces consistent results across 3 runs (high-variance prompts are noise). (3) You can articulate one failure mode it diagnoses. If a prompt fails any of those three checks, drop it.

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About the author. Joao da Silva is co-founder of friction AI with Camilla Wirth. friction AI tracks brand visibility across ChatGPT, Claude, Perplexity, and Gemini. The clients are SaaS and DTC brands. Joao writes about AI search, entity recognition, and how to get recommended by LLMs. Connect with him on LinkedIn.