Augmenting Large Language Models with Reverse Proxy Style Retrieval Augmented Generation for Higher Factual Accuracy

The increasing reliance on artificial intelligence for generating human-like text has brought attention to the critical issue of factual accuracy in language models. Introducing a novel approach, this research augments the Llama model with a reverse proxy-style Retrieval Augmented Generation (RAG) mechanism, significantly enhancing the factual accuracy and coherence of the generated text. By dynamically incorporating relevant and up-to-date information from diverse external data sources, the RAG-augmented Llama model addresses the inherent limitations of static training data, thereby generating more reliable and contextually appropriate responses. The experimental evaluation, using precision, recall, F1-score, BLEU, and ROUGE metrics, demonstrated substantial improvements, affirming the effectiveness of the proposed system. The findings reveal the potential of integrating retrieval mechanisms with generative models to achieve higher quality language generation, offering valuable insights for future research and practical applications in fields where precision and reliability are paramount.