Text-to-SQL

Text-to-SQL allows non-technical users to formulate queries in natural language, which are then automatically converted into SQL. This field is rapidly evolving, driven by generative models such as GPT-4o and Sonnet, along with initiatives like QueryGPT from Uber, which aim to improve performance by incorporating more context and evaluating the quality of generated queries.

These technologies leverage intelligent agents to interpret user intent and generate queries tailored to specific databases. However, despite significant progress, they still face limitations. Common issues include translation errors in SQL, such as inventing nonexistent columns, and challenges in handling more complex queries involving multiple joins or subqueries. Additionally, contextual understanding of data remains imperfect.

Theodo’s point of view

We recommend a cautious evaluation of these technologies. While they can boost productivity for simple queries, they are not yet reliable for complex environments without human supervision.