DB-GPT: Large Language Model Meets Database This paper proposes DB-GPT, a database optimization framework based on large language models (LLMs), which includes automatic prompt generation, database-specific model fine-tuning, and database-specific model design and pre-training. The framework aims to address challenges in using LLMs for database tasks, such as providing appropriate prompts, capturing both logical and physical database information, and training database-specific LLMs while ensuring privacy. Preliminary experiments show that DB-GPT achieves good performance in tasks like query rewrite and index tuning. The paper discusses three main strategies for using LLMs in database tasks: input prompt generation, database-specific LLM fine-tuning, and database-specific LLM design and pre-training. Input prompt generation aims to generate additional text information to guide LLMs in understanding task requirements. Database-specific LLM fine-tuning updates network parameters to memorize task-specific knowledge. Database-specific LLM design and pre-training require a large number of database-specific training samples to learn network parameters. The paper also addresses several challenges in using LLMs for database tasks, including how to generate input prompts, how to fine-tune LLMs for database tasks, how to design a database-specific LLM, how to provide sufficient high-quality data for fine-tuning, and how to efficiently fine-tune LLMs. Additionally, the paper discusses how to ensure the validity of LLM output and how to train LLMs with database data while ensuring data privacy. The paper concludes that LLMs have potential in handling database tasks, but there are still many opportunities for further research. The authors believe that the use of LLMs will continue to benefit the field of database systems, including text2SQL, SQL2Plan, database diagnosis, and data tuning.DB-GPT: Large Language Model Meets Database This paper proposes DB-GPT, a database optimization framework based on large language models (LLMs), which includes automatic prompt generation, database-specific model fine-tuning, and database-specific model design and pre-training. The framework aims to address challenges in using LLMs for database tasks, such as providing appropriate prompts, capturing both logical and physical database information, and training database-specific LLMs while ensuring privacy. Preliminary experiments show that DB-GPT achieves good performance in tasks like query rewrite and index tuning. The paper discusses three main strategies for using LLMs in database tasks: input prompt generation, database-specific LLM fine-tuning, and database-specific LLM design and pre-training. Input prompt generation aims to generate additional text information to guide LLMs in understanding task requirements. Database-specific LLM fine-tuning updates network parameters to memorize task-specific knowledge. Database-specific LLM design and pre-training require a large number of database-specific training samples to learn network parameters. The paper also addresses several challenges in using LLMs for database tasks, including how to generate input prompts, how to fine-tune LLMs for database tasks, how to design a database-specific LLM, how to provide sufficient high-quality data for fine-tuning, and how to efficiently fine-tune LLMs. Additionally, the paper discusses how to ensure the validity of LLM output and how to train LLMs with database data while ensuring data privacy. The paper concludes that LLMs have potential in handling database tasks, but there are still many opportunities for further research. The authors believe that the use of LLMs will continue to benefit the field of database systems, including text2SQL, SQL2Plan, database diagnosis, and data tuning.