Leveraging HyDE and RAG in Gemma LLM Framework for Enlightened Deep Learning Chatbots

Abstract

Chatbots have revolutionized human–computer interaction by providing intelligent, conversational assistance in various areas, including academic support, healthcare, and technical support. Cutting-edge chatbots utilize Natural Language Processing (NLP) and Machine Learning to generate responses that make sense and are relevant to the context. It is challenging to provide precise and knowledge-rich answers to complex questions, and this remains a significant challenge. Retrieval-Augmented Generation (RAG) systems address this challenge by combining generative language models with information retrieval techniques. This study presents an advanced RAG framework incorporating Hypothetical Document Embedding (HyDE) query transformation and a fine-tuned Gemma 7B model, achieving significant improvements in efficiency and accuracy. HyDE enhances precision by generating hypothetical reports from input queries, bridging semantic gaps in complex domains. This approach enables the system to retrieve domain-specific documents aligned with user intent, significantly improving response quality. Experimental results demonstrate that this system yields RAG implementations that improve retrieval precision, contextual relevance, accuracy, and response fluency. HyDE integration notably enhanced retrieval accuracy for intricate queries, while the fine-tuned Gemma 7B model efficiently produces coherent and accurate responses. This research highlights the transformative potential of combining advanced query transformation techniques with fine-tuned language models like Gemma.

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