Retrieval Augmented Code Generation

Retrieval-Augmented Code Generation (RACG) aims to improve code generation by large language models (LLMs) through the integration of external knowledge sources. Current research focuses on enhancing LLMs' ability to leverage information from diverse sources like documentation, code repositories, and online forums, often employing active retrieval strategies and iterative refinement techniques to improve code accuracy and address context limitations. This approach holds significant promise for automating software development tasks, improving code quality, and enabling more efficient and robust information extraction from textual data.