Bridging the SQL Skills Gap: How LLM-Based Text-to-SQL Boosts Team Productivity
from Healthy.io (Israel)
About speaker
Head of Research, Healthy.io
Sivan Biham is a Machine Learning researcher with experience in Computer Vision, Reinforcement Learning and Deep Learning among others. Currently, she is the Head of Research at Healthy.
About speakers company
Using colourimetric analysis, computer vision, and AI we transform the smartphone camera into a clinical-grade medical device. Our computer vision algorithms and unique calibration method can analyse urine tests and chronic wounds using a variety of devices and under different lighting conditions, making testing as easy as snapping a picture.
Abstracts
Imagine empowering your entire team—technical and non-technical alike—to easily query databases using everyday language. With LLM-based Text-to-SQL tools, you can shorten query development time, enabling faster decision-making and collaboration. In this talk, I’ll show you how to implement Text-to-SQL in your own database, optimize it with prompt engineering techniques, and leverage database structure for better results. It's about closing the SQL skills gap and boosting team productivity by making data accessible to everyone.
In modern data-driven organizations, the ability to quickly access and analyze data is key to making informed decisions. However, querying databases often requires specialized SQL skills, which limits access to technical teams and increases the development cycle for data insights. In this talk, we will explore how Text-to-SQL technologies can dramatically enhance team productivity by shortening query development time. In this talk, I will share practical ways to implement Text-to-SQL for your own database, starting with the basics and improving results using prompt engineering techniques and database structure representation. By democratizing data access and improving cross-functional collaboration, Text-to-SQL can reduce bottlenecks, speed up decision-making, and enhance overall organizational efficiency.
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