What 239 Million Prompts Tell Us About How People Search in AI Platforms

The largest dataset ever assembled on how people actually use AI search platforms reveals something important: the way people formulate questions when talking to AI is fundamentally different from how they type keywords into Google. Semrush's database of over 239 million prompts — the queries people are entering into platforms like Gemini, ChatGPT, and Google AI Mode — offers an unprecedented window into AI search behavior. This post examines what the data reveals about prompt patterns, search intent evolution, and what these insights mean for your content strategy in 2026.

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Why Prompt Data Is Different from Keyword Data

Keyword data tells you what words people type into a search bar. Prompt data tells you what questions people ask when they expect a conversation. The difference is more significant than it might appear, and it has direct implications for how you should be thinking about content.

Traditional keywords are compressed expressions of intent — users have learned over decades that search engines respond better to concise, structured queries than to natural questions. "best CRM software small business" rather than "what's a good CRM tool for a ten-person sales team that doesn't have a big budget and needs something that integrates with Gmail." The keyword is the user accommodating the machine.

AI prompts reverse this dynamic. Users talk to AI platforms the way they'd talk to a knowledgeable colleague — with full context, specific constraints, follow-up questions, and natural language complexity. The machine accommodates the human. This produces a very different data set, and content that aligns with how people actually think about their problems — rather than how they've learned to compress those problems into keyword-sized packages — performs fundamentally differently in AI search.

This shift is also reshaping how search engines present information to users. As discussed in The Google AI Takeover: What It Means for Your Traffic, AI-generated summaries and conversational search experiences are fundamentally changing how users discover content, interact with search results, and engage with websites.

Key Patterns in AI Search Prompt Behavior

Analysis of large-scale prompt data reveals several consistent patterns that have direct implications for content strategy.

Prompts are significantly longer than keywords

The average AI platform prompt is substantially longer than the average search keyword. While traditional search queries average three to four words, AI prompts frequently run to fifteen, twenty, or more words. Users include context, constraints, and qualifications that never appear in keyword searches. "Affordable SEO tools for a freelance content marketer who mainly works with e-commerce clients" is a prompt — "SEO tools for freelancers" is the keyword equivalent. The content that serves the prompt needs to speak to specificity and context in ways that keyword-optimized content often doesn't.

Intent is more explicit in prompts

In keyword search, inferring intent from a short query requires significant interpretation. In AI prompts, users typically state their intent directly. They explain what they're trying to accomplish, what constraints they're working within, and often what they've already tried. This explicitness is valuable for content strategy because it tells you exactly what problem your content needs to solve — and how to frame the solution in terms that match the user's own language.

Comparison and recommendation prompts dominate commercial categories

In commercial and B2B categories, the most common prompt types are comparison queries and recommendation requests. Users ask AI platforms to compare options, recommend solutions for specific situations, and evaluate trade-offs between alternatives. This is the prompt equivalent of the traditional "best of" and "vs" keyword patterns — but with far more context and specificity. Content that explicitly addresses comparisons and use-case-specific recommendations performs disproportionately well in AI citation for these query types.

Follow-up and refinement prompts reveal consideration depth

AI platforms enable multi-turn conversations, and the follow-up prompts users ask after an initial response reveal important information about what they actually need to know. Common follow-up patterns include requests for more specific examples, questions about implementation details, and requests to compare the initial recommendation against a specific alternative they've thought of. Content that anticipates these follow-up questions — addressing them proactively within a single piece — is more likely to be the comprehensive resource AI platforms cite rather than one of several sources users need to consult.

Problem-first framing is more common than solution-first

In keyword search, users often search for solutions directly — "email marketing software," "SEO platform." In AI prompts, users more commonly describe their problem first and ask for solutions — "I'm struggling to keep track of my email open rates across multiple client accounts, what tool would help with this." Content that is framed around the problem it solves — rather than the features of the solution — aligns better with how users are formulating their AI search queries.

What This Means for Your Content Strategy

The prompt data patterns above point to specific, actionable changes in how to approach content creation for AI search visibility.

Write for problems, not products

Lead your content with a clear articulation of the problem your reader is experiencing, before introducing the solution. This problem-first framing matches how users are describing their needs in AI prompts and increases the probability that your content is surfaced when AI platforms respond to problem-oriented queries. The solution — including tool recommendations and how-to guidance — flows naturally once the problem is clearly established.

Build specificity into your content structure

Generic content that addresses broad topics without specificity is increasingly underperforming in AI search. Content that speaks to specific use cases, specific audience types, specific constraints, and specific contexts matches the detailed prompt language users are employing. A guide to "SEO tools for e-commerce freelancers with multi-client workflows" will be cited more consistently for relevant prompts than a generic "best SEO tools" roundup, even if the latter has stronger traditional ranking signals.

This shift toward contextual and intent-driven discovery is also changing how search engines evaluate and surface content. As explored in Google Indexing in the AI Age: Is Traditional SEO Still Keeping Up?, AI-powered indexing systems are increasingly prioritizing content depth, relevance, and contextual alignment over traditional keyword-focused optimization alone.

Anticipate and answer follow-up questions

Structure your content to address the natural follow-up questions your audience would ask after reading the initial answer. This means going beyond the surface-level response to cover implementation details, common objections, edge cases, and comparison points that users routinely explore in multi-turn AI conversations. Content that addresses these follow-up questions comprehensively becomes the single authoritative resource rather than one stop in a multi-source research process.

Use Prompt Research to discover your audience's actual language

One of the most valuable applications of Semrush's 239-million-prompt database is discovering the exact language your target audience is using when they describe their problems and search for solutions in AI platforms. The Prompt Research tool in Semrush's AI Visibility Toolkit surfaces the prompts driving AI answers in your category — including metrics like LLM topic volume and difficulty, top brands being cited, and the sentiment of responses. Using this data to inform your content language and structure is one of the most direct paths to improving AI search visibility available.

For businesses looking to turn those AI search insights into measurable lead generation and customer engagement workflows, HubSpot can help bridge the gap between audience research and execution. By combining CRM data, marketing automation, and content personalization, businesses can better align their messaging with the real conversational language users are now using across AI-driven search platforms.

Prompt Data as a Competitive Intelligence Tool

Beyond informing your own content strategy, prompt data reveals competitive dynamics that aren't visible through traditional keyword research. Which brands are being cited most frequently for the prompts that matter most in your category? Which prompt types are dominated by one or two players, and which represent open opportunities? What attributes are AI platforms associating with each brand when they appear in responses?

These competitive insights from prompt data give you a different and complementary view of your competitive landscape from what traditional keyword competitive research provides. They show you where the AI search battle for your category is actually being fought — and whether you are currently a participant or a bystander in that battle.

People Also Ask

How is prompt research different from keyword research?

Keyword research identifies the terms people type into search engines and analyzes their search volume, competition, and ranking difficulty. Prompt research identifies the natural language questions people ask AI platforms, analyzes which brands and sources are cited in responses, and reveals the intent and context patterns driving AI search behavior. The two are complementary — keyword research informs traditional SEO, while prompt research informs AI visibility strategy. The most complete content strategies use both.

Can I use traditional keyword research tools for AI search optimization?

Traditional keyword research tools provide useful input for AI search optimization — particularly for understanding topic areas and intent patterns — but they don't capture the full picture of AI search behavior. Prompt data reveals longer, more contextual queries, comparison and recommendation patterns, and multi-turn conversation dynamics that keyword data doesn't surface. Dedicated prompt research tools, like those in Semrush's AI Visibility Toolkit, provide the AI-specific data layer that traditional keyword tools lack.

How often does prompt data change, and how should I update my content strategy accordingly?

Prompt patterns evolve continuously as AI platforms expand their user bases, as new AI features launch, and as user familiarity with AI search deepens. Quarterly prompt research reviews are a reasonable cadence for most brands — enough to catch meaningful shifts in how your audience is using AI platforms without requiring constant strategy revision. For fast-moving categories or brands undergoing significant competitive pressure in AI search, monthly reviews are more appropriate.

Final Thoughts

The 239-million-prompt database underlying Semrush's AI Visibility Toolkit represents something genuinely new: a data-driven window into how people actually think about and articulate their needs when using AI platforms. The patterns that emerge from that data — longer, more contextual, problem-first, comparison-heavy — describe a fundamentally different search behavior from the keyword patterns that have driven SEO strategy for decades.

Content strategies that are built around these prompt patterns — that speak to problems with specificity, anticipate follow-up questions, and use the language of real user queries rather than compressed keyword phrases — will be consistently better positioned for AI search citation than strategies that treat AI search as an extension of keyword-based SEO. The data makes the path clear. The work is in following it.

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