SQL AI empowers non-technical users to access data insights and make data-driven decisions without needing expert support. Learn how off-the-shelf solutions like Locusive simplify the implementation of three crucial best practices for querying databases with AI, enabling teams to work more efficiently.
Workflow Engineering: Bridging the Gap Between Teams and Technology
Whether you operate a specialized development team or wear your data science hat to build predictive models to assess credit loan risk, manage customer churn, reduce hospital admissions or more, you’ll likely be using SQL to retrieve the data you need from your databases. Oftentimes you’ll need to write intricate SQL queries to get the results you want, which is time-consuming and tedious.
In an attempt to streamline this process, many database professionals and developers turn to AI-enabled query generation tools to automate SQL creation. However, SQL AI isn’t a magic bullet and it’s important to understand the limitations of such tools before adopting them for use in production.
The key to SQL AI is context. By providing the database schema and query text to the tool, the user can give instructions in their own language and get tailored SQL output with a high degree of accuracy.
This approach is also the only way to ensure that the SQL generated by AI is safe for production usage. This is because queries that are not safe for production can cause performance degradation or even data loss. It’s critical that any SQL generated by an AI-enabled query tool is reviewed and optimized by a human analyst before being used in production.