Developing a Fit-for-Purpose Best Practice Knowledge Handbook Using Generative AI

Abstract

The Construction Industry Institute (CII) has produced extensive research shown to deliver cost, schedule, and safety benefits within the construction industry.However, low visibility and limited accessibility to the research have hindered its widespread adoption.This paper introduces a generative artificial intelligence (GenAI) framework designed to enhance the accessibility and utilization of CII research for industry practitioners.The proposed framework employs a Retrieval-Augmented Generation (RAG) approach by integrating CII best practice (BP) research reports into a large language model (LLM) to identify relevant insights.First, a hybrid method combines qualitative analysis with GenAI-driven questionanswering to extract critical findings ("golden nuggets") from each BP report.These nuggets are then used to generate detailed action items (DAIs) by LLM.The validation process involves using an LLM judge method complemented by subject matter experts (SMEs) review.Lastly, an executive summary and frequently asked questions (FAQs) were generated for each BP by feeding GNs and DAIs information to LLM.As a result, a comprehensive BP knowledge handbook was generated.This handbook showcases the potential of GenAI in construction knowledge extraction in a passive way.Additionally, this study will also present a prototype where users can interact with the GenAI chatbot in an active way for knowledge harvesting and communication.This study contributes to advancing human-AI interaction in construction knowledge management, offering a scalable and user-friendly solution for bridging the gap between research and practice.

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