2026 AI Prompting Skill Survey Template

An AI prompting skill survey measures how people interact with large language models — covering technique knowledge, skill self-assessment, learning habits, and perceived productivity impact. It produces benchmarkable data on prompting proficiency across roles, helping organizations understand where prompt engineering capability currently stands in their workforce or target audience.

This survey takes approximately three minutes to complete. It includes ten structured fields — dropdowns, radio buttons, checkboxes, and a single open-text tip field — plus an optional email capture. The short format improves completion rates while still collecting enough data to produce meaningful benchmarks on AI prompting habits and skill distribution.

The survey asks respondents to select all techniques they actively use from a checklist that includes role-playing, few-shot prompting, chain-of-thought, system prompts, structured formatting, iterative refinement, retrieval-augmented generation (RAG), and natural language prompting. This breadth allows you to compare technique adoption rates across skill levels and roles in your collected dataset.

For reliable benchmark data, distribute across multiple roles and experience levels rather than a single team. Embedding the link in AI-focused newsletters, Slack communities, or LinkedIn posts targeting practitioners tends to generate diverse respondents. Anonymous responses encourage honest self-assessment, particularly for the skill level and productivity impact fields where respondents may otherwise over-report proficiency.

Yes — this template spans prompt engineering technique adoption, AI tool preferences by platform, self-rated AI literacy, and beliefs about the future relevance of prompt engineering skills. It bridges practical usage data with forward-looking sentiment, making it useful for both AI literacy research and workforce readiness assessments tied to large language model deployment.