Which categories saw the most prompt demand in Q2, and what the shift from Q1 tells us about where AI use is heading.
Key findings
#1
Marketing stays top
Marketing-related prompts held the top position for the third consecutive quarter, accounting for 13% of all prompt demand on the platform.
+67%
Education surge
Education prompts grew 67% quarter-on-quarter as AI tutoring went from early-adopter behavior to mainstream student use.
+340%
Vibe Coding spike
Demand for Cursor, Lovable, and Claude Code workflow prompts spiked 340% in Q2 as no-code and AI-first development reached a mainstream tipping point.
3 in 5
Use ChatGPT by default
When users search for a tool-specific prompt, 58% specify ChatGPT. Gemini is a distant second at 19%, with Claude at 14% but growing fastest in writing categories.
Shares represent the proportion of all prompt searches on TopFreePrompts.com. Trend arrows compare to the prior equivalent period.
Based on tool-specific searches (e.g., "ChatGPT for marketing") on TopFreePrompts.com. Tool-agnostic searches are excluded from this breakdown.
The clearest breakout category of Q2. Non-technical founders and solo developers are now building full products using natural language, and they need prompts for architecture planning, debugging, and feature spec writing.
End-of-school-year timing and growing institutional acceptance of AI in classrooms drove education prompts to their highest Q2 share ever. Concept explanation and essay feedback are the top sub-types.
As more people deploy personal AI agents, demand for well-structured system prompts with memory, persona, and tool-use instructions grew sharply. This is now a distinct category rather than a subset of coding.
Data represents the composition of prompt demand on TopFreePrompts.com for Q2 2026 (April 1 to June 30). Category shares reflect the relative proportion of prompt searches across the platform. Growth percentages compare Q2 2026 to Q1 2026. Tool share data reflects tool-specific search intent on the platform. All figures are estimates based on internal platform signals combined with publicly available industry data.
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