If AI answers are the new front door for discovery, your visibility depends on the questions people actually ask assistants and whether those answers name, cite, and link you. This guide gives a pragmatic, research-grounded method for choosing prompts that reveal your real standing across ChatGPT, Gemini, Claude, and Perplexity, then turning those findings into page-level fixes.
For background on why this is different from SEO, see our posts: The Shift From “Keywords” to “Entities” and Beyond One Prompt: Why AI Visibility Demands Broader Thinking and Better Links.
What makes a good “AI prompt” (not a search query)
Search queries are fragments (“best crm startups”). AI prompts are instructions and intents (“Act as a buyer’s guide. For a 10-person startup, compare HubSpot vs. Pipedrive, focus on onboarding time and native email sync.”). They reflect how people naturally converse and ask for reasoning, steps, comparisons, or citations. Google’s Gemini guidance explicitly frames prompting as iterative, structured instruction, not keyword matching.
Two other realities shape prompt design:
- People increasingly “talk to models” … literally. Voice input is mainstream (≈20% of internet users globally; billions of assistants in use via DemandSage), so spoken-style prompts matter.
- Users collaborate and iterate with assistants; prompts aren’t one-shot. HCI research documents multi-turn behaviors and “prompt families” for planning, writing, and evaluation.
Implication: Your test set must include conversational, instructional, spoken/voice, and a small control group of terse, search-like prompts to benchmark coverage across styles.
Why prompts for visibility must target entities and citations
AI systems privilege entities (things) over strings (words). That shows up in which domains they cite. A 2025 analysis of 6.8M citations across ChatGPT, Gemini, and Perplexity found ~86% came from brand-managed sources (your site, listings you control). In other words, assistants mostly cite your pages if they trust them.
This aligns with research that augmenting LLMs with knowledge graphs and structured facts increases factuality and retrieval quality, models reason better when entities and relationships are explicit.
Implication: The “right prompts” are those that pressure-test how clearly your entity is defined and how often assistants choose your page as the citable source, not how many keywords you rank for. For a deeper primer, see Inside the Black Box: How AI Decides Which Brands to Cite.
A robust prompt taxonomy that mirrors real user behavior
Design your test set around intent buckets and modalities. This captures how people actually ask models and what assistants need to decide who to cite.
Intent buckets (what users try to do)
- Identity: “What is {Brand}? Who is it for?” (use sparingly; sanity check only)
- Category framing: “Explain {category} for {persona}. When is it a fit?”
- Comparison: “Compare {category} options for {persona}; pros/cons and pricing watchouts.”
- Alternatives: “Alternatives to {solution} when you need {constraint}.”
- Task/JTBD: “Step-by-step plan to {job}. Include tools, docs, and pitfalls.”
- Pricing/Plans: “Typical pricing models for {category}; which to pick for {persona}.”
- Trust/Proof: “Reputable {category} vendors that show {proof metric} or compliance.”
- Integration: “Tools that work with {integration} to achieve {job}.”
- Support/Troubleshooting: “Common issues with {category} and how to fix them.”
- Geo (only if relevant): “Vendors serving {region} for {use-case} with local support.”
- Edge/Discovery: “Common misconceptions about {category}; how to avoid them.”
Modalities (how they ask)
- Conversational (“I’m comparing… help me decide…”)
- Instructional (“Create a checklist… outline a 30-day plan…”)
- Spoken/voice (punctuation-light, “compare {category} for startups whats best”)
- Terse/search-like (small control cohort only)
Why this matters: Gemini, Perplexity, and others surface citations differently by task mode; spoken and instructional phrasing frequently change which domains get linked.
How many prompts do you actually need?
Start with a Baseline Set (≈36 prompts):
- 2–4 identity checks (just enough to catch schema/entity bugs)
- 4–6 category/alternatives
- 4–6 comparison
- 3–5 task/JTBD
- 2–3 pricing/support
- 2–3 integration
- 1–2 geo (only if clearly relevant)
- 1–2 edge/discovery
- Modalities mix: ~50% conversational, 20% instructional, 10–15% search-like (control), 5–10% spoken
Then add an Extended Set (≈96 prompts) that paraphrases the strongest intents and expands modality diversity to test stability (do you still get cited when phrasing shifts?). This multi-prompt approach avoids the “single-prompt mirage” we warned about in Beyond One Prompt.
Building the set from a homepage (black-box method)
When you can’t rely on internal knowledge, you can still generate realistic prompts that reflect how buyers ask assistants:
- Extract onsite signals: brand, categories, personas, jobs-to-be-done, integrations, proof points, locations (from visible copy + schema).
- Map to the taxonomy above; generate prompts per bucket with both conversational and instructional phrasing.
- Include spoken variants to mirror voice behavior (it’s material at population scale).
- Keep branded prompts ≤10%, they’re sanity checks, not visibility drivers.
- Add terse controls to benchmark whether assistants can still find you from “search-like” phrasing.
How to evaluate what the prompts reveal (and avoid false comfort)
What to record per answer:
- Mention vs. citation (appeared by name vs. earned a link)
- Primary link (did the assistant pick the right page?)
- Coverage type (direct, partial, indirect)
- Evidence used (which sources; brand-owned vs third-party)
- Model notes (Gemini’s sources panel, Perplexity’s inline references, etc.)
Why this matters: A brand-first bias in citations is real, but only if your pages provide the best fit for the question. The 86% brand-owned sources stat is an opportunity only when your entity definition and page intent match are clean.
Quality & risk checks:
- Independent studies in sensitive domains show correctness and sourcing vary by model and task, evaluate across engines and over time.
- You can use “LLM-as-a-judge” techniques to rate summaries consistently when human review is impractical (use with clear rubrics and spot audits).
Provenance & policy awareness:
- Understand where model knowledge comes from (training + retrieval) when interpreting results and planning fixes.
- Be aware of ongoing debates around scraping and citation norms (context for outlier results, especially with news).
A step-by-step methodology you can run every quarter
- Assemble prompts using the taxonomy and counts above (Baseline + Extended).
- Run across engines (ChatGPT, Gemini, Claude, Perplexity).
- Log outcomes per prompt: mention, citation, correct page, competing sources, and snippet.
- Cluster the misses by cause:
- No mention → weak entity definition; add a top-of-page Answer Block.
- Cited wrong page → fix internal links, anchors, on-page headings.
- No citation → add scannable facts, table summaries, and neutral comparisons.
- Outdated info → add updated on stamps and changelogs.
- Competitor dominance → publish side-by-side buyer guides with plain-language criteria.
- Ship page-template fixes (not just one-offs): definitions, fact strips, comparison tables, linkable actions, and minimal aligned schema (Organization, Product/Service, FAQ, HowTo).
- Re-run the same cohorts to measure delta in share of AI answers and primary citation rate (not just “did we appear”). For why this matters structurally, revisit Beyond One Prompt.
Example: translating research into prompt choices
- Because assistants cite brand-controlled pages most often, include task/JTBD and comparison prompts that a well-built product or service page should win, these maximize your odds of becoming the “citable source.”
- Since structured knowledge boosts factual stability, add explain prompts that should pull your definition and schema-anchored facts verbatim, then verify they do.
- Because voice usage is significant, include spoken variants and check if answers still cite you (voice often compresses detail and changes the page Gemini/Perplexity select).
Pitfalls to avoid
- Over-weighting branded prompts. If “What is {Brand}?” fails, fix schema and definitions, but don’t spend your runs here.
- Testing only one modality. If you skip instructional or spoken phrasing, you’ll miss how assistants change sources by task mode.
- Chasing model quirks instead of page fixes. Models evolve; entity clarity + scannable facts + stable anchors are durable advantages.
A compact starter list (copy/adapt)
- “Explain {category} for {persona}. When is it a fit and when isn’t it?”
- “Compare options in {category} for {persona}; table with setup time, integrations, and real limits.”
- “Create a 30-day plan to {job}. Link steps to credible sources.”
- “What pricing models exist for {category}? Which fits {persona} with {constraint}?”
- “Alternatives to {solution type} if you need {outcome} without hiring an agency.”
- “Common pitfalls with {category}; how to avoid them.”
- Voice: “best {category} for {persona} under {constraint} pros and cons”
Run these across engines; confirm whether your canonical page is the primary citation.
The mindset shift
Choosing the “best prompts” isn’t about guessing what a bot might say, it’s about auditing how assistants reason over your entity. Models are trained on public, partner, and user-provided data, and they surface sources differently by mode and product. Treat prompts as diagnostic tools that expose whether your pages are the obvious, citable answer for how people actually ask.
When you pair the right prompt mix with page templates that emphasize definitions, scannable facts, clean schema, and linkable actions, visibility improves and stays improved.
That’s the core of AI-first visibility. Start with your Baseline Set, run it everywhere, fix what the prompts reveal, then re-run.
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