Seven Questions Before Every Prompt: A Pre-Use Checklist for LLM-Assisted Academic Work

Background: The rapid diffusion of AI-generated content (AIGC) tools has shifted the central question for university teachers and students from whether to use large language models (LLMs) to how to use them. Yet institutions face a structural gap: policy documents specify what is permitted or prohibited, while technical literature describes what models can do — and neither tells an individual user what concrete actions to perform before each use. In high-reliability domains such as surgery and aviation, precisely this gap is closed by a checklist.Objective: This paper introduces the first systematic pre-use checklist for LLM-assisted academic work — a seven-item verification instrument that translates regulatory requirements and technical constraints into executable actions, bridging the policy layer and the technology layer.Methods: To derive the checklist, we combine a structured review of Chinese legislation, ministerial regulations, and university AIGC policies (consolidated into a three-norm compliance model: Permitted / Prohibited / Disclosure) with an analysis of five structural limitations of LLMs documented in the technical literature (mapped to four countermeasure strategies and an Ask–Plan–Craft tri-layer interaction model). Checklist design follows the evidence-based methodology of high-reliability domains: items are short, binary-verifiable, and target the steps most often skipped. The instrument is validated through a documented case study — the development of a local text-to-speech workbench (qwen-tts-app) with a commercial agentic tool.Results: The checklist comprises seven pre-use gates spanning task scoping, plan-before-execution, task decomposition, auditable computation, mandatory output verification, data isolation, and AI-use disclosure; each item maps explicitly onto institutional and regulatory requirements. In the case study, every checklist item corresponded to a concrete, auditable action, and six of ten documented iteration failures were directly attributable to structural LLM limitations — confirming that mandatory output verification is indispensable.Conclusion: Compliance first, boundary awareness second, method third. The seven-item checklist operates as an actionable bridge between what institutions require and what individuals do, reducing individual cognitive load and institutionalizing tacit best practices. We offer it as a protocol awaiting its outcome study.