Operational training safety is an important guarantee for the combat effectiveness and standardized duty and training of primary-level firefighting forces. With the increasing number of complex and high-risk emergencies, safety instruction for operational training of primary-level firefighting forces is facing greater challenges. According to data from the National Fire and Rescue Administration, the annual average incidence of high-rise building fires, hazardous chemical leaks, high-altitude rescues, water and ice rescues, and other emergencies has increased by approximately 5% over the past two years. At present, safety instruction for operational training of primary-level firefighting forces is generally confronted with prominent problems, such as limited practical training scenarios, rigid teaching modes, difficulty in memorizing safety regulations, and inefficient review of typical cases. Traditional teaching methods can hardly meet the practical operational needs of “all-hazard types and major emergencies.” To address the pain points and difficulties in primary-level safety instruction, this study takes lightweight artificial intelligence technology as the research medium and, based on the Operational Training Safety Guidelines for National Comprehensive Fire and Rescue Forces (Trial) (hereinafter referred to as the Safety Guidelines), investigates the adaptation advantages and implementation pathways of three lightweight tools, namely Jimeng AI, Doubao AI, and Jianying AI, in safety instruction for operational training.