In this text, I’ll examine how large language models (LLMs) can convert natural language into SQL, making query writing more accessible to non-technical users. The discussion will include practical examples that showcase the benefit of developing LLM-based solutions. We’ll also cover various use cases and exhibit the method by creating a straightforward Slack application. Constructing an AI-driven database querying system involves several critical considerations, including maintaining security, ensuring data relevance, managing errors, and properly training the AI. On this story, I explored the quickest solution to tackle these challenges and shared some suggestions for establishing a solid and efficient text-to-SQL query system.
Currently, it’s hard to consider any technology more impactful and widely discussed than large language models. LLM-based applications are actually the newest trend, very like the surge of Apple or Android apps that after flooded the market. It’s used in every single place in BI space and I previously wrote about it here [1]
