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Unlocking the power of large language models
In this article, I will 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 ease of developing LLM-based solutions. We’ll also cover various use cases and demonstrate the process by creating a simple Slack application. Building an AI-driven database querying system involves several critical considerations, including maintaining security, ensuring data relevance, managing errors, and properly training the AI. In this story, I explored the quickest way to tackle these challenges and shared some tips for setting up a solid and efficient text-to-SQL query system.
Lately, it’s hard to think of any technology more impactful and widely discussed than large language models. LLM-based applications are now the latest trend, much like the surge of Apple or Android apps that once flooded the market. It is used everywhere in BI space and I previously wrote about it here [1]
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