Augmenting Code Sequencing with Retrieval-Augmented Generation (RAG) for Context-Aware Code Synthesis
This project presents a novel system designed to enhance code generation by leveraging Retrieval-Augmented Generation, Grounding techniques, and Prompt Parameters, which represents a significant advancement in automating code generation from natural language descriptions.
Abstract
The growing demand for efficient code generation has driven research into improving Large Language Models (LLMs). This project presents a novel system designed to enhance code generation by leveraging Retrieval-Augmented Generation (RAG), Grounding techniques, and Prompt Parameters. RAG integrates external knowledge to enrich code outputs, while Grounding methods improve the model’s ability to interpret language with real-world context. Prompt Parameters offer flexibility, enabling customized outputs based on user preferences. These methods were implemented and tested on various code generation tasks, resulting in contextually relevant and accurate outputs. The proposed system streamlines software development workflows, reduces errors, and fosters better collaboration between developers and machine-assisted coding tools. Ultimately, this approach represents a significant advancement in automating code generation from natural language descriptions.