NL2SQL

Enterprise AI usually begins with a deceptively simple request: ask the system a business question and get an answer. Then reality enters, politely carrying a knife. The relevant data is not in one table. The schema is incomplete. The user’s intent depends on personal preference. A term such as “Bay Area” needs external knowledge. A PDF, a web page, an image, and a database record all matter. Someone wants the answer explained, filtered, joined, visualized, and revised after a follow-up question. The demo looked like a chatbot; the production requirement looks suspiciously like distributed systems engineering. ...

TL;DR for operators VeriMinder is a useful reminder that the most dangerous analytics failure is not always a bad SQL query. Sometimes the SQL is correct, the dashboard loads, the stakeholder nods, and the decision is still built on a question that should never have passed quality control. The paper introduces VeriMinder, an interactive system that sits before or alongside a natural-language-to-SQL workflow and checks whether the user’s question is biased, under-specified, or poorly aligned with the decision being made.1 Its target is not SQL syntax. Its target is analytical intent. ...