OPTIMIZING LATENCY AND ACCURACY TRADE-OFFS IN LARGE-SCALE RETRIEVAL-AUGMENTED GENERATION PIPELINES

Retrieval-Augmented Generation (RAG) pipelines have developed as a successful paradigm to increase the reality base of huge language models (LLMs) through the combination with retrieval methods in the generative model.Nonetheless, the addition of the retrieval and generation modules presents a basic conflict between latency and the accuracy within larger-scale implementation due to the demands of real-time responsiveness.In this paper, a detailed study of the optimisation strategies available to strike these trade-offs in RAG pipelines is given.We look at latency factors, such as retrieval model complexity, indexing models, and query expansion operators, and we evaluate these factors with regard to their overall effect on the accuracy of the answer provided on a run of benchmark collections in a variety of domains.Our hybrid optimization method consists of adaptive retrieval depth, query relevance score thresholds and model distillation, allowing us to achieve stable near-optimal accuracy and minimise latency by 45% in high-throughput applications.Scalability of the proposed approach with various hardware and software setups, a GPU and CPU cluster, is later proved through experimental tests.The paper will also present a latency-accuracy Pareto frontier model that can assist system designers in making decisions when deploying a machine on the basis of informed choices.We find that parameter tuning, caching, and pipeline parallelization have a large performance bonus with limited impact on output quality, thus RAG is a competitive option for latency-constrained applications, including conversational artificial intelligence, augmenting search, and domain-specific knowledge representations.