A Framework for Automated Code Generation and Data Transformation Using LLMs

Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large language models (LLMs) to address these challenges by developing a comprehensive framework that seamlessly integrates LLM capabilities for generating accurate and optimised code, constructing complex SQL queries, and automating data transformations. By combining advancements in natural language understanding, algorithm synthesis, and debugging, the proposed framework will enable developers to efficiently process natural language problem descriptions and translate them into executable code. This research seeks to streamline workflows across diverse domains, from software engineering to data engineering, advancing innovation and reducing the complexity of programming tasks.