AUSTIN, Texas – The large language models that underlie generative AI offer an opportunity to help everyone involved in buildings — architects, engineers, construction professionals and facility managers — access building systems that converse with one another on optimizing development, maintenance and compliance issues. “It’s time for us to think about not looking at predictive buildings, but more towards conversational buildings,” Nan Ma, assistant professor of architectural engineering at Worcester Polytechnical Institute, said at the ASHRAE annual conference this week. Ma, founding director of the Laboratory for Healthy, Environmental, and Resilient Buildings, or HERB-Lab, at WPI, led a group of global researchers on a Building and Environment study earlier this year that looks at how LLMs can help make sustainable, intelligent and human-centric buildings a reality. “Things like energy use, comfort, occupancy, maintenance logs, control behavior [and] control logics” are the kinds of factors that LLMs could process to enable building systems to better help managers maintain operations better, she said. For example, a building manager, maintenance team or facility manager could ask an LLM why the eastern zone of a building is so hot in the afternoon, and receive an answer based on information that has been logged over years. The LLMs will process previously siloed data on temperature, energy consumption and occupancy patterns, among other things, and then share that information across building systems to produce an answer along with its context, Ma said. The idea of using LLMs to enable building systems to converse with one another solves one of the biggest problems faced by building operators who want to use AI to help them manage their facilities better, what’s known as a “great handoff problem.” The term refers to the separation among sources of data created in a building’s lifecycle and the designs, principles, ideas and