The Question Words That Dominate AI Search: 'What', 'How', 'Best', and 'Vs' Breakdown for 2026

Four question words drive the vast majority of commercial queries in AI search engines: what, how, best, and vs. Each triggers a different response pattern across ChatGPT, Perplexity, Gemini, and Claude. If your brand isn't built into the answers to these query types, you're invisible at the exact moment users are making decisions. Here's what each word means for your GEO strategy.

AI search is no longer a curiosity. 37% of consumers now begin their searches with AI tools as of January 2026. ChatGPT alone crossed 1 billion monthly active users in June 2026, the fastest any app has ever hit that milestone. The question is not whether your category is being searched in AI engines. It is whether you appear when it is.

Why These Four Question Words Define AI Search Behaviour

Each of the four dominant question word types maps to a distinct user intent, and AI engines respond to each one differently. Understanding the intent behind each word is the first step to building content that gets cited rather than skipped.

Traditional SEO treated queries as keywords to rank for. GEO treats them as intents to satisfy. The distinction matters because AI engines don't just retrieve a page; they synthesise an answer. That synthesis is shaped entirely by the intent signal in the query. A "what" question asks for a definition or explanation. A "how" question asks for a process. A "best" query asks for a recommendation. A "vs" query asks for a comparative verdict. Each format needs a different content structure to be citable.

This is not theoretical. Reports show AI Overviews are most frequently triggered by complex, multi-part questions including "how to combine," "compare," and "best way to" queries, along with instructional and product comparison searches. The query vocabulary of AI search users is already different from traditional search, and it's skewing toward these four words.

What Does 'What' Trigger in AI Engines?

"What" queries are definitional. They ask AI engines to explain, categorise, or describe something. For brands, these queries represent the top of the funnel: the moment a user is orienting themselves in a category before they go looking for a solution.

When someone asks "What is the best project management software?" or "What is a customer data platform?", AI engines pull from content that defines the category clearly and authoritatively. If your brand owns a definition in your category, you have a structural advantage. If you don't, you'll likely be absent from the explanation and only appear, if at all, as a footnote example.

The content format that wins "what" queries is the answer-first definition page. First sentence defines the term. Following paragraphs give context, use cases, and examples. AI engines can extract the first sentence as a standalone snippet. If that sentence mentions your brand or category in a way that positions you as the authority, you've given the model a reason to cite you.

How Do 'How' Queries Behave Differently Across Platforms?

"How" queries are instructional. They ask for a process, a method, or a step-by-step guide. These are the queries where AI engines most clearly replace the traditional click to a blog post. The user wants the answer right there in the response, not a link to go read one.

This creates a counterintuitive active. The more useful your how-to content is, the less likely the user is to visit your site after the AI answers their question. Over 65% of informational queries now resolve without a user visiting a website, driven by AI-generated summaries. But the trade-off is worth it. Being cited as the source of an answer builds brand association with competence in that domain. Users remember who the AI credits, even when they don't click through.

Platform behaviour differs here. Perplexity cites numbered sources for every answer and is strict about grounding its responses in retrieved content. ChatGPT synthesises more freely but still links to sources when search is triggered. For "how" queries, Perplexity rewards structured, numbered process content. ChatGPT rewards concise, scannable guides. You need both formats to cover your bases.

Why 'Best' Queries Are the Highest-Stakes Category for Brands

"Best" queries are where brand selection happens. When a user types "best CRM for small business" or "best running shoes for flat feet," they are ready to evaluate options. The AI's answer is effectively a shortlist. If your brand is on it, you're in consideration. If you're not, you may as well not exist for that user at that moment.

These queries are also the hardest to win organically. AI engines are systematically biased toward earned media, meaning third-party reviews, comparison sites, editorial roundups, and category publications. A University of Toronto research paper on GEO found that AI search engines exhibit a systematic and overwhelming bias toward earned media over brand-owned and social content, a stark contrast to Google's more balanced approach.

This means your own product page claiming you are the "best" does almost nothing for AI visibility on these queries. What moves the needle is appearing consistently in external review content, industry comparisons, and editorial lists. G2, Capterra, industry publications, and Reddit threads are more useful for "best" query visibility than your own marketing site. We track this regularly using BrandPrompts-generated prompt sets, and the pattern holds across categories.

The content structure that wins "best" queries externally is the ranked comparison listicle. Sites that publish these with structured criteria, clear methodology, and genuine product detail get cited. Thin "top 10" lists with no original analysis do not.

What Makes 'Vs' Queries a Unique Opportunity?

"Vs" queries are comparison-intent. "HubSpot vs Salesforce for startups," "ChatGPT vs Perplexity for research," "Notion vs Obsidian for notes." Users asking these have already narrowed their consideration set. They want a verdict, not a list. AI engines synthesise a comparative answer, and the brands they mention in that answer get disproportionate awareness.

The opportunity here is two-sided. You want to appear in "vs" queries where you're one of the two named brands. You also want to appear in "vs" queries where neither brand is yours but your category is the subject. If a user asks "Salesforce vs HubSpot for enterprise?" and the AI responds by mentioning a third option like your CRM as better suited for mid-market, that's a citation you couldn't have engineered through traditional SEO.

Winning "vs" queries requires genuine comparative content. First-hand analysis of how your product differs from competitors, with specific feature comparisons and honest trade-off assessments, is what AI engines pull. Vague claims of superiority don't get cited. Specific, substantiated comparisons do.

How the Four Query Types Map to Content Formats

Each query type needs a specific content structure to be citable. Here's how we map them:

How to Build Your GEO Strategy Around These Query Types

Winning AI search in 2026 means having a deliberate content and distribution strategy for each of these four query types. Most brands over-index on "what" content (definitional blog posts) and completely neglect "best" and "vs" queries, which is exactly where purchase decisions are made.

Here's a practical sequence:

1. Audit your current AI visibility by querying ChatGPT, Perplexity, and Gemini with "what is [your category]," "best [your category] for [your target persona]," "how to [main use case your product solves]," and "[your brand] vs [top competitor]." Note where you appear and where you don't.
2. Map the gaps. If you're invisible on "best" queries, the problem is almost always earned media coverage, not your own site content. Prioritise getting into third-party review roundups and editorial lists in your category.
3. Build a structured prompt set that covers all four query types across your topic pillars and target markets. BrandPrompts does this from real search data, so your tracking prompt set reflects how users actually phrase queries rather than how you assume they do.
4. Assign a content format to each gap. "How" gaps get solved with better structured guides. "What" gaps get solved with cleaner definition pages. "Vs" gaps often require publishing honest comparative content or earning mentions in independent comparison posts.
5. Track visibility per query type over time. If your "best" visibility improves after a PR push or a new G2 review campaign, you have evidence the tactic is working. Without per-query-type tracking, you're flying blind.

The earned media bias is the most important structural reality to accept. For "best" and "vs" queries especially, your owned content is a weak lever. Third-party coverage is where the citations come from. Plan your distribution accordingly.

Frequently Asked Questions

What surpasses AI in search effectiveness?

Nothing surpasses AI for synthesis and explanation tasks, but traditional Google search still outperforms for live information (prices, news, local results), official documents, and situations where users want to compare multiple sources directly. A sensible search strategy uses both. Google for real-time and local; AI engines for research, comparison, and guidance.

How do you dominate AI search results in 2026?

Dominating AI search in 2026 requires two parallel tracks. The first is on-page: structure every piece of content with direct answers at the top, use strict heading hierarchies, include comparison tables and numbered lists, and write for scanability rather than scroll depth. The second is off-page: earn citations in third-party reviews, editorial roundups, Reddit discussions, and industry publications, because AI engines weight earned media heavily over brand-owned content. Neither track alone is sufficient.

What are the top strategies for ranking in AI search results?

The highest-impact strategies are: earning consistent third-party coverage so AI engines associate your brand with your category, structuring content so the first paragraph under each heading is a standalone answer, building FAQ sections that mirror real user prompts, and tracking your visibility across multiple AI engines rather than assuming one platform tells the whole story. Each engine (ChatGPT, Perplexity, Gemini, Claude) has different source preferences and retrieval behaviour, so visibility on one doesn't guarantee visibility on another.

Why does 'best' query visibility require a different strategy than 'how' visibility?

"How" queries can be won through well-structured brand-owned content, because AI engines retrieve instructional detail from direct sources. "Best" queries require earned media, because AI engines treat evaluative claims from a brand about itself as inherently biased. Reviews, comparison sites, and editorial selections are the sources AI engines trust for evaluation queries. Your product page saying you're the best is not a credible signal. A G2 review or an industry publication listing you is.

How many prompts do you need to track AI visibility reliably?

Industry benchmarks are still emerging, but our recommendation at BrandPrompts is at least 30-50 prompts per topic-market combination for statistically meaningful visibility data. With fewer prompts, the natural variation in AI responses makes it hard to tell whether a change in your visibility score reflects a real trend or just random fluctuation. If you're tracking a single market across four query types, that's a minimum of 120 to 200 prompts for reliable measurement. See the BrandPrompts pricing page for how we structure prompt sets by scale.