# Uber put QueryGPT, a natural-language-to-SQL tool, into production for operations and support teams, reporting query authoring time down from about 10 to about 3 minutes (~300 daily users)

Page: https://flyvolo.ai/en/changes/ev-20240919-data-analyst-1
Date: 2024-09-19 · Stage: Deployment
Can this record move a task judgement? yes
Occupation: https://flyvolo.ai/en/careers/data-analyst

## Scope

One company, limited release; Uber's platform runs about 1.2 million interactive queries a month, so this is a small share. The post itself flags hallucinated tables and columns and prompt quality as open problems.

## Source

- Uber — engineering blog — https://www.uber.com/en-US/blog/query-gpt/ (primary source)

## What this means

A deployment record on the query-writing task from inside the workflow: Uber's natural-language-to-SQL tool is in production for operations and support teams, with authoring time reported down from about 10 to about 3 minutes for around 300 daily users. It supports the task page's mechanism on the deployment layer — and the post itself names hallucinated tables and columns and prompt quality as open problems.

## What it does not show yet

300 users against roughly 1.2 million interactive queries a month is a small corner of one company, and the users are ops and support staff, not analysts; the timing figure is self-reported. Uber's own post lists hallucinated tables and columns as open problems, so this is not evidence the tool is reliable on an uncurated warehouse, and nothing in it concerns analyst headcount.

## How to verify it yourself

Take the five queries you wrote most recently and try each as a plain-English question in whatever SQL assistant your BI tool or warehouse offers, recording which ran correctly without edits; the failures will almost all be on tables with no documented definitions, and that list is your team's missing semantic layer.


More: https://flyvolo.ai/llms.txt