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Let’s Build a Text-to-SQL Project Using LLM
Build a quick LLM project
If you have ever written SQL queries or know how they work, you very well know that writing SQL isn’t always the best use of time, especially when you just want a quick answer from your data.
Well, the idea of querying databases in natural language has been around for a while, but the early iterations have been slow and prone to giving completely wrong answers.
So I wanted to see if I could build a modern, agentic Text-to-SQL pipeline that was fast, accurate, and most importantly, self-evaluative.
The goal is simple. Type a question in plain English and get a clean data table and the raw SQL query back in milliseconds. To make things interesting, I built the system around a local Formula 1 database tracking drivers, teams, and race results.
I will discuss how I built it and how the architecture works, and I will also provide the GitHub link to the entire codebase.
The Tech Stack
I wanted to keep things as simple as possible. To achieve the performance and usability goals, I decided to select a…
