Project overview
This project developed a Text-to-SQL pipeline that allows users to query relational databases using natural language, with minimal technical expertise required. The system converts user input into SQL, executes it, and automatically returns the results. A modular, multi-step LLM prompting strategy ensures accurate SQL generation for complex queries and multi-turn conversational interactions.
Problems solved
The system enables non-technical users to query structured databases using natural language, without SQL knowledge. It automatically generates accurate SQL queries and returns clean, structured results in real time. Context-aware follow-up queries are supported by preserving conversation history and reusing prior query logic. Overall, the solution improves data accessibility, usability, and interaction efficiency.
