How to pick the 20 queries that decide your AI search strategy

Most startups tracking AI search visibility make the same mistake: they track everything or they track nothing. Both kill your ability to learn what's working.

The middle path is picking 20 queries. Not 200. Not 5. Twenty is enough to spot patterns, small enough to manually test monthly, and sufficient to build a feedback loop between content decisions and citation performance.

This is not keyword research. It's query selection for a different game. The goal is not ranking position. It's citation probability across ChatGPT, Perplexity, Google AI Overviews, and Gemini. The queries you pick determine what you measure, what you optimize, and ultimately whether your content gets used by AI engines when your buyers ask questions.

Here's how I do it for Series A and Series B companies with small teams.

Start with buyer questions, not search volume

Traditional keyword research starts with volume and competition. AI search query selection starts with buyer intent and answer format.

The queries that matter most are the ones your prospects actually type into ChatGPT or Perplexity when evaluating solutions. These are rarely your highest-volume SEO terms. They're longer, more specific, and often phrased as natural questions.

Ask your sales team: what questions do leads ask in discovery calls? Ask your customer success team: what confusion shows up in onboarding? Ask your support team: what keeps coming up in tickets?

Those questions are your starting point. Write them down exactly as spoken. Don't optimize the phrasing yet.

Good examples from a B2B SaaS client I worked with last year:

• "How do I connect Salesforce to Slack without Zapier?"
• "What's the difference between event-based and time-based workflows?"
• "Can I trigger a webhook when a deal stage changes?"

Bad examples (too vague, no clear intent):

• "CRM integration"
• "Workflow automation"
• "API documentation"

AI engines answer questions, not topics. Your query list should reflect that.

Layer in the queries where competitors already get cited

Run 10 to 15 of your core topics through ChatGPT, Perplexity, and Google (with AI Overviews enabled). Note which competitors appear in the answers and which URLs get cited.

Then reverse-engineer the queries that trigger those citations. If a competitor's comparison page gets cited when you search "best project management tools for remote teams," that query goes on your list.

This is not about copying competitor content. It's about identifying the queries where citation behavior is already established. If AI engines are citing someone for a query, it means the query has commercial value and a citation slot exists. Your job is to compete for that slot.

Use incognito mode or a clean browser. Personalization skews results. You want to see what a cold prospect sees, not what Google thinks you want to see.

Prioritize queries with multiple citation opportunities

Some queries trigger a single cited source. Others trigger five or six. You want the latter.

Perplexity typically cites 8 to 12 sources per answer. Google AI Overviews cite 3 to 6. ChatGPT varies depending on whether search is enabled, but multi-source answers are common for comparison, evaluation, and how-to queries.

Test each candidate query and count the citations. If a query consistently returns answers with four or more cited sources, it's a better target than a query that returns one or two. More citation slots mean more chances to appear, and more opportunities to learn what content structures work.

Queries that produce list-based answers ("top 5," "best options," "comparison of") and how-to answers with steps tend to have the highest citation density. Definitional queries ("what is X") often return fewer citations because the answer is shorter.

Separate informational, comparison, and solution-aware queries

Not all queries serve the same function in your funnel. Your 20-query list should include a mix:

Informational queries (early stage, educational):

• "How does webhooks work in SaaS tools?"
• "What is API rate limiting?"

Comparison queries (mid-funnel, evaluation):

• "Zapier vs Make vs n8n for workflow automation"
• "Best CRM for startups under 20 people"

Solution-aware queries (late stage, close to decision):

• "How to set up [your product] with HubSpot"
• "Does [your product] integrate with Notion?"

A healthy query list has roughly 40% informational, 40% comparison, 20% solution-aware. Adjust based on your business model. If you're unknown, weight informational higher. If you have strong brand awareness, weight solution-aware higher.

The reason this matters: citation behavior differs by intent. Informational queries favor authoritative explainers and third-party sources. Comparison queries favor structured lists and review aggregators. Solution-aware queries favor your own docs, assuming they're well-structured and accessible.

Test manually, monthly, in all four engines

Pick one day each month (I use the first Monday). Open ChatGPT, Perplexity, Google AI Mode, and Gemini in separate browser windows. Run all 20 queries. Record the results in a spreadsheet.

Track:

• Was your brand mentioned? (Yes/No)
• Was your content cited? (Yes/No, plus URL if cited)
• Citation position (primary, supporting, or not cited)
• Competitor presence (which competitors appeared, in what order)

This takes 90 minutes. It's manual, repetitive, and boring. It's also the most reliable way to understand what's actually happening at small scale.

Automated tools exist, but most are expensive and built for agencies running hundreds of queries. For 20 queries, a spreadsheet and discipline beat a $500/month SaaS tool.

The data you collect tells you three things:

1. Which queries you're winning (defend these)
2. Which queries competitors own (study their content structure)
3. Which queries have no clear winner (opportunities to claim)

Replace queries that don't move

Every quarter, review your list. If a query hasn't changed in three months (you're never cited, or you're always cited in the same position), consider replacing it.

Static queries don't teach you anything. The goal of this exercise is learning, not vanity metrics. If you're stuck at zero citations for "best CRM for startups" and nothing you've published has moved the needle, swap it out for a related but less competitive query where you have a better chance of seeing movement.

Conversely, if you've locked in the primary citation slot for a query and held it for three months, you've won that query. You can keep tracking it for defense, but you might learn more by adding a new query to test a different content angle.

The 20-query list is not static. It's a working document that evolves as your content library grows and your understanding of citation patterns deepens.

What this looks like in practice

I ran this process with a Series A logistics SaaS client in Q4 2025. We started with 18 queries (they added two more in month two).

After three months:

• 6 queries showed consistent citations (win, defend)
• 4 queries showed competitor dominance with no movement (study, then replace)
• 5 queries showed citation probability improving (double down, optimize further)
• 3 queries showed inconsistent results across engines (longer-term targets, needs format testing)

That pattern is typical. Roughly one-third of your queries will show traction within 90 days if your content is structured correctly. One-third will be long-term projects. One-third will teach you what doesn't work.

The companies that win AI search in 2026 are not the ones with the most content. They're the ones with the tightest feedback loop between query selection, content structure, and citation measurement.

Twenty queries. Tested manually. Every month. That's the loop.

If you want help identifying your first 20 queries or running an AI search audit to see where you currently stand, book a 30-minute strategy call. I'll walk you through the process and show you what's already working (or not) in your category.