A Retrieval‐Augmented Generation System for Accurate and Contextual Historical Analysis: AI‐Agent for the Annals of the Joseon Dynasty - Lee, Jeong Ha; Ali, Ghazanfar; Hwang, Jae‐In (Computer animation and virtual worlds, 2025)
ABSTRACT In this article, we propose an AI‐agent that integrates a large language model (LLM) with a retrieval‐augmented generation (RAG) system to deliver reliable historical information from the Annals of the Joseon Dynasty through both objective facts and contextual analysis, achieving significant performance improvements over existing models. For an AI‐agent using the Annals of the Joseon Dynasty to deliver reliable historical information, clear source citations and systematic analysis are essential. The Annals, an official record spanning 472 years (1392–1897), offer a dense, chronological account of daily events and state administration that shaped Korea's cultural, political, and social foundations. We propose integrating a LLM with a RAG system to generate highly accurate responses based on this extensive dataset. This approach provides both objective information about historical figures and events from specific periods and subjective contextual analysis of the era, helping users gain a broader understanding. Our experiments demonstrate improvements of approximately 23 to 50 points on a 100‐point scale compared with the GPT‐4o and OpenAI AI‐Assistant v2 models.