BHGPT: Multiple AI Models for Built Heritage Knowledge Retrieval

BHGPT (Built Heritage Generative Pretrained Transformers) is an AI-driven framework designed to improve knowledge retrieval and management in the built heritage context. While traditional methods struggle with heterogeneous sources, inconsistencies, and evolving historical data, this study explores customized GPT, Fine-Tuned BHGPT, and an AI Assistant with Retrieval-Augmented Generation (RAG) to enhance the accessibility and interpretability of heritage information. This study adopts a domain knowledge first and operational approach, in which discipline-specific concepts and terminology guide the organization of information and the structuring of model outputs. The approach begins with a customized GPT model and prompt engineering to test the model's response behavior. After that, fine-tuning the model on specific datasets enhances domain-specific accuracy. Finally, the AI Assistant with RAG integrates structured HBIM data and unstructured archival sources, enabling dynamic querying and cross-referencing of historical and architectural knowledge. The framework is tested on the Sanctuary of Hercules and the Former Segrè Papermill in Tivoli, evaluating its performance across historical evolution, architectural aspects, and interdisciplinary knowledge. Results indicate that Fine-Tuned BHGPT significantly improves site-specific knowledge extraction, while AI Assistant with RAG provides the most flexible and adaptive responses by linking multiple data sources. However, its accuracy is dependent on data availability and retrieval mechanisms. Overall, the contribution clarifies the utility of the BHGPT framework for heritage professionals, enhancing accessibility through natural language querying, producing structured and interoperable outputs, and supporting transparent, source-grounded interpretation.