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This paper introduces a Retrieval-Augmented Generation (RAG) methodology to adapt GPT-4o for scientific scoping reviews, specifically examining mixed forest plant functional traits and ecosystem services. By integrating a curated knowledge base of cutting-edge literature and established contextual information with a meta-prompt, the system augments human-guided inquiry to identify effective plant trait combinations. The hybrid approach successfully generated insightful outputs and is presented as a transferable framework for other scientific domains.
AI-powered scoping reviews can now efficiently identify optimal plant trait combinations for ecosystem services, bridging the gap between cutting-edge research and practical application.
This study proposes and evaluates a methodology for adapting a Large Language Model (LLM) using the Retrieval-Augmented Generation (RAG) framework to support scientific scoping reviews. As a case study, we focused on mixed-species forest plant functional traits and their influence on ecosystem service provision. The LLM (GPT-4o), implemented via Microsoft Copilot, was adapted using contextual information, a meta-prompt, and a curated knowledge base. The contextual layer consisted of well-established knowledge from reputable authors and publishers, while the knowledge base was constructed from cutting-edge scientific literature retrieved from leading academic databases. By combining AI-based agents specifically designed for scoping review tasks with a human-guided inquiry approach, the methodology produced insightful outputs relevant to identifying effective plant trait combinations. The proposed approach is transferable to other scientific domains. Finally, we emphasize the need for interdisciplinary collaboration to develop accessible, domain-specific AI tools that can effectively support scientific research.