Mitigating Prompt Dependency in Large Language Models: A Retrieval-Augmented Framework for Intelligent Code Assistance

Background: The implementation of Large Language Models (LLMs) in software engineering has provided new and improved approaches to code synthesis, testing, and refactoring. However, even with these new approaches, the practical efficacy of LLMs is restricted due to their reliance on user-given prompts. The problem is that these prompts can vary a lot in quality and specificity, which results in inconsistent or suboptimal results for the LLM application. Methods: This research therefore aims to alleviate these issues by developing an LLM-based code assistance prototype with a framework based on Retrieval-Augmented Generation (RAG) that automates the prompt-generation process and improves the outputs of LLMs using contextually relevant external knowledge. Results: The tool aims to reduce dependence on the manual preparation of prompts and enhance accessibility and usability for developers of all experience levels. The tool achieved a Code Correctness Score (CCS) of 162.0 and an Average Code Correctness (ACC) score of 98.8% in the refactoring task. These results can be compared to those of the generated tests, which scored CCS 139.0 and ACC 85.3%, respectively. Conclusions: This research contributes to the growing list of Artificial Intelligence (AI)-powered development tools and offers new opportunities for boosting the productivity of developers.