Context-Driven Chatbot Development: Leveraging Zephyr-7b with RAG for Improved Response Accuracy

In recent years, the demand for AI-driven conversational agents has increased significantly across various industries. Traditional generative models, while capable of producing human-like responses, often struggle with factual accuracy and context retention. Retrieval Augmented Generation (RAG) presents a novel approach to enhance the performance of AI chatbots by combining the strengths of both retrieval-based and generative models. The paper focuses on the development of an AI-driven chatbot using a RAG framework, integrating the Zephyr-7b model, to serve the needs of the ICT Academy of Kerala (ICTAK). The chatbot is engineered to deliver precise and contextually relevant responses to user queries by integrating advanced language models with structured data extracted from the ICTAK website. The data, which was meticulously scraped and processed, ensures that the chatbot's knowledge base remains current and accurately reflects the ICTAK's services and operations. A critical challenge addressed by this work is the issue of hallucinations in large language models (LLMs), where models may generate seemingly plausible yet incorrect or irrelevant information. To counteract this, the paper employs various chunking methods that enhance the chatbot's capability to retrieve and generate accurate responses. This AI Chatbot incorporates cutting-edge techniques in natural language processing, including document retrieval, context compression, and re-ranking. This multi-faceted approach ensures that the chatbot not only provides accurate responses but also delivers them in a contextually relevant manner.