Imagine asking your database complex questions in plain English and getting instant answers. That's the promise of Text-to-SQL, a field of AI research focused on automatically translating natural language questions into SQL queries. But what happens when the AI gets it wrong? New research introduces a clever technique called Decomposed Automation Correction (DAC) to make AI-powered Text-to-SQL more reliable. Traditional methods often struggle to directly correct errors in generated SQL. DAC tackles this by breaking down the problem into smaller, manageable parts. Think of it like proofreading an essay – it's easier to spot mistakes when you focus on grammar and style separately before looking at the overall structure. DAC focuses on two key sub-tasks: entity linking (identifying the relevant database tables and columns) and skeleton parsing (grasping the underlying logic of the question). By correcting these individual components, DAC ensures that the final SQL query is more accurate. Tests on benchmark datasets like Spider, Bird, and KaggleDBQA show that DAC significantly boosts the performance of Text-to-SQL systems, even with open-source language models. This improvement is especially noticeable with smaller AI models, which are more prone to making mistakes. DAC opens up exciting possibilities for making databases more accessible to everyone. Imagine business analysts, marketers, and even non-technical users easily querying data without writing a single line of SQL. While DAC makes significant progress, challenges remain. Future research will focus on further refining entity linking and skeleton parsing. The ultimate goal is to create AI-powered Text-to-SQL systems that are robust enough to handle the complexities of real-world data and user queries.