Why 90% Accuracy in Text-to-SQL is 100% Useless

The use of Large Language Models for Text-to-SQL applications requires near-perfect accuracy, as anything less, even 90%, erodes user trust and renders the system useless for enterprise self-service analytics. Building a robust system involves a complex Retrieval-Augmented Generation (RAG) pipeline, and while platforms like Google BigQuery are integrating these components natively, they do not solve the core accuracy problem. Rigorous evaluation is the most critical component, moving beyond simple string matching to metrics like Execution Accuracy (EX), which compares the results of the generated query against a ground truth query. Modern benchmarks like Spider 2.0 are essential for testing enterprise readiness, as they introduce real-world complexities like massive database schemas and dialect diversity, on which many current models fail.