A growing trust crisis
You throw a task at an AI, and seconds later it spits out a dozen-line SQL blob. You copy and paste it, and it runs. But do you really feel confident about it?
That’s probably the daily reality for every data analyst and developer today.
Today, AI can generate runnable SQL. The problem is that you don’t know whether you can trust it. Once queries involve deeply nested window functions and multi-level subqueries, they become difficult to review, debug, maintain, and port across databases.
Research has shown that when LLMs lack sufficient schema context and domain knowledge, they can produce hallucinated outputs, such as incorrect table joins, flawed aggregation logic, or missing critical filters. According to dbt’s 2026 benchmark, even the most advanced LLMs achieved only 64.5% accuracy on Text2SQL tasks. In other words, one out of every three SQL queries generated by AI may contain errors.
