The Database Has a New User—LLMs—and They Need a Different Database

Matvey Arye's article explores the development of a self-describing database using PostgreSQL to improve AI agents' ability to generate accurate SQL queries. By embedding semantic context into database schemas through natural language descriptions, the initiative aims to address the traditional lack of context in databases that confounds large language models (LLMs). Early experiments show a significant improvement in SQL generation accuracy, up to 27%, when using LLM-generated semantic catalogs. This approach involves creating a structured representation of database metadata and business logic, stored in version-controlled YAML files for peer review and governance, which are then indexed for semantic search. The article outlines a step-by-step process for implementing this system, emphasizing the importance of semantic context in SQL generation and proposing a roadmap for future enhancements towards a self-learning catalog.

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