Din SQL

Din SQL, or Decomposed In-Context SQL, represents a family of approaches aiming to improve the accuracy and efficiency of converting natural language queries into SQL code using large language models (LLMs). Current research focuses on enhancing LLMs' reasoning capabilities through techniques like query decomposition, multi-agent collaboration, retrieval-augmented generation, and prompt engineering, often incorporating schema linking and iterative refinement to handle complex queries and large databases. These advancements are significant because they address limitations of previous methods, improving the usability and reliability of natural language interfaces for databases across various applications.