Date of Award
5-2025
Degree Type
Honors College Thesis
Academic Program
Computer Science BS
Department
Computing
First Advisor
Nick Rahimi, Ph.D.
Advisor Department
Computing
Abstract
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.
Copyright
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Recommended Citation
Shrestha, Yaju Gopal, "Cache-Augmented Generation in RAG Pipelines: Fast and Memory-Efficient Approach to Multi-Agent Knowledge Query Systems" (2025). Honors Theses. 1011.
https://aquila.usm.edu/honors_theses/1011