The field of software development and construction safety is witnessing significant advancements with the integration of artificial intelligence and large language models. Researchers are exploring innovative approaches to generate acceptance criteria, statements of work, and software requirements specifications, aiming to improve development efficiency, reduce manual effort, and enhance accuracy. The use of multi-modal data, retrieval-augmented generation, and modular architectures is becoming increasingly prominent in these efforts. Furthermore, the release of new datasets, such as those focused on requirements quality and construction safety, is facilitating more sound and collaborative research efforts. Noteworthy papers include: Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs, which proposes a novel approach to generate acceptance criteria from multi-modal requirements data. ReqInOne: A Large Language Model-Based Agent for Software Requirements Specification Generation, which presents an LLM-based agent that generates software requirements specifications with improved accuracy and consistency.