In this article, I’ll discuss 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 show the ease of developing LLM-based solutions. We’ll also cover several use cases and demonstrate the process by building a simple Slack app. Building an ai-powered database query system involves several critical considerations, such as maintaining security, ensuring data relevance, handling errors, and properly training the ai. In this story, I explored the quickest way to address these challenges and shared some tips for setting up a robust and efficient text-to-SQL query system.
Lately, it is hard to think of a more impactful and widely discussed technology than big language models. LLM-based apps are now the latest trend, just like the boom in Apple or Android apps that once flooded the market. It is used everywhere in the business intelligence arena and I previously wrote about it here (1)
