Member-only story
HVSR: Setting the Bar for Text-to-SQL with Human-Verified Repositories
When you ask an AI to generate SQL from a natural language question, what you get back often looks impressive until a database expert examines it. Hidden within that syntactically correct query might be missing joins that would return incorrect data, absent indexes that would cause performance issues, or complex subqueries where simpler approaches exist.
In production environments, these inefficiencies aren’t just theoretical problems , they can bring critical systems to a halt or lead to business decisions based on flawed data.
Why are data analysts still the kings of writing database queries?
Domain expertise. Business context. Optimization instinct. While AI stumbles with complexity, human experts craft elegant, efficient solutions. They don’t just write queries that work. They create queries that excel.
This is today’s data dilemma. Organizations race to democratize access with text-to-SQL AI tools. These tools make databases approachable for everyone. But they consistently miss the optimizations that human experts instinctively apply.
The challenge is clear: How do you make data accessible through AI without sacrificing the query quality your business depends on?
