Accelerating RAG Systems: A Performance-Oriented Systematic Mapping - Silva, João Gabriel J. da; Oliveira, Sávio S. T. de; Galvão Filho, Arlindo R. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Optimizing latency and inference efficiency has become critical for the deployment of Retrieval-Augmented Generation (RAG) systems in production environments. While recent methods have explored GPU acceleration, keyvalue (KV) caching, and hierarchical indexing, their impact remains fragmented across studies. This paper presents a performance-oriented systematic mapping of optimization techniques targeting time-to-first-token (TTFT), latency, and caching efficiency metrics in RAG pipelines. Following the Kitchenham protocol and operationalized through the Parsifal platform, 34 studies were selected from an initial pool of 147. The analysis reveals growing research focus on prefix-aware KV reuse, asynchronous retrieval, and GPU-accelerated decoding, but also highlights gaps in unified evaluation and multilingual scalability. The findings provide a consolidated view of current strategies and identify research directions for scalable, latency-sensitive RAG systems.