Ref: https://aiwithmike.substack.com/p/text2sql-the-art-of-teaching-machines

Mike Erlihson's Substack post explores the complexities of Text2SQL, the process of enabling machines to translate natural language questions into SQL queries. The author highlights that this task involves more than just syntax, touching upon context, user intent, and ambiguity, areas where large language models (LLMs) often mimic understanding rather than possess genuine comprehension. Erhlihson emphasizes the need for multi-layered systems with components for input interpretation, schema mapping, generation, validation, and user feedback to create robust Text2SQL applications. The piece further discusses practical challenges like synonym variations, subjective user queries, and messy database schemas that impact the effectiveness of these systems. Ultimately, the article envisions Text2SQL not as full automation, but as a collaborative tool that empowers both technical and non-technical users to interact with data conversationally and iteratively.