Cache-Augmented Generation in RAG Pipelines: Fast and Memory-Efficient Approach to Multi-Agent Knowledge Query Systems

This thesis presents an implementation and evaluation of Cache-Augmented Generation (CAG) for knowledge query systems, building upon the approach introduced by Chan et al. (2024). Traditional Retrieval-Augmented Generation (RAG) systems (Lewis et al., 2020) face challenges including high latency, excessive memory usage, and complex infrastructure requirements. By implementing a cache-augmented architecture that preloads relevant knowledge and eliminates real-time retrieval, our approach significantly improves response time while reducing resource requirements. The research demonstrates the effectiveness of CAG through a comprehensive implementation for The University of Southern Mississippi's chatbot system, achieving a 49.02% improvement in response time compared to traditional RAG approaches. Our findings suggest that CAG provides a streamlined and efficient alternative to conventional RAG pipelines, particularly for applications with constrained knowledge bases. This research contributes to the growing field of efficient large language model deployment in resource-conscious environments and offers practical insights for implementing multi-agent knowledge systems in educational settings.