Comparative Analysis of RAG, Fine-Tuning, and Prompt Engineering in Chatbot Development

This paper examines the integration and comparative effectiveness of Retriever-Augmented Generation (RAG), fine-tuning, and prompt engineering in the development of advanced chatbots. By employing domain-specific fine-tuning, the study addresses contextual misunderstandings and inaccuracies prevalent in base Large Language Models (LLMs). RAG enhances chatbot functionality by incorporating real-time data retrieval, ensuring relevance in dynamically changing environments. Prompt engineering is utilized to refine input prompts, thereby optimizing the accuracy of responses. Employing the “openassistant-guanaco” dataset from Hugging Face, this research assesses the performance improvements offered by these methodologies, both quantitatively and qualitatively. The fine-tuned model outperforms other methods with an accuracy of 87.8 % and a BLEU score of 0.81, proving its effectiveness in generating the most relevant responses. In contrast, while the RAG with LLM approach shows promising results with a reasonable accuracy of 84.5 %, the Prompt Engineering method, though slightly less effective with an accuracy of 83.2 %, still maintains competitive performance. This study highlights the unique and combined strengths of these technologies, contributing valuable insights into their synergistic potential for enhancing chatbot interactions