The paper "DB-GPT: Large Language Model Meets Database" by Xuanhe Zhou, Zhaoyan Sun, and Guoliang Li explores the integration of large language models (LLMs) into database systems to optimize various tasks such as query rewrite and index tuning. The authors identify several challenges in using LLMs for database optimization, including the need for appropriate prompts, capturing both logical and physical database characteristics, and handling strict constraints and privacy requirements. To address these challenges, they propose a LLM-based database framework called DB-GPT, which includes automatic prompt generation, DB-specific model fine-tuning, and DB-specific model design and pre-training. Preliminary experiments show that DB-GPT achieves good performance in query rewrite and index tuning tasks. The paper also discusses the advantages of LLMs over existing AI4DB approaches, such as higher transfer capability, user-friendly interfaces, and prior knowledge learning. The authors propose methods for generating input prompts, fine-tuning LLMs, and designing database-specific LLMs, and provide empirical results to demonstrate the effectiveness of their approach. The source code and datasets are available on GitHub.The paper "DB-GPT: Large Language Model Meets Database" by Xuanhe Zhou, Zhaoyan Sun, and Guoliang Li explores the integration of large language models (LLMs) into database systems to optimize various tasks such as query rewrite and index tuning. The authors identify several challenges in using LLMs for database optimization, including the need for appropriate prompts, capturing both logical and physical database characteristics, and handling strict constraints and privacy requirements. To address these challenges, they propose a LLM-based database framework called DB-GPT, which includes automatic prompt generation, DB-specific model fine-tuning, and DB-specific model design and pre-training. Preliminary experiments show that DB-GPT achieves good performance in query rewrite and index tuning tasks. The paper also discusses the advantages of LLMs over existing AI4DB approaches, such as higher transfer capability, user-friendly interfaces, and prior knowledge learning. The authors propose methods for generating input prompts, fine-tuning LLMs, and designing database-specific LLMs, and provide empirical results to demonstrate the effectiveness of their approach. The source code and datasets are available on GitHub.