Leveraging generative AI through prompt engineering for corpus construction and in-depth intelligent interpretation of ancient texts

Abstract High-quality domain corpora constitute the cornerstone of computational humanities research. This study investigates the foundational methodologies and optimization strategies that leverage large language models (LLMs) in conjunction with prompt engineering to facilitate the construction and in-depth analysis of classical text corpora. The research systematically categorizes prevalent prompt frameworks and presents a comprehensive prompt optimization protocol, delineating common chain-of-thought enhancement approaches. It examines the application of prompt engineering-driven LLMs to address conventional natural language processing tasks for ancient texts. The study demonstrates the viability of prompt engineering methodologies based on large models for the intelligent processing and application of classical texts, offering relevant Artificial Intelligence (AI) digital humanities product exemplars as efficacious references for developing high-caliber corpora in the computational humanities domain. Future investigations should contemplate further exploration of AI technology’s potential in humanities scholarship, fostering interdisciplinary collaboration across humanities and social sciences disciplines, including history, literature, philosophy, computer science, and information science. Ongoing attention should be directed toward the technological impact on promoting and preserving exemplary traditional Chinese culture, ensuring judicious technological implementation alongside the safeguarding of traditional cultural knowledge and values.