Energy-Efficient Retrieval-Augmented Generation: A Systematic Review of Retrieval, Reranking, Caching, and Context-Management Strategies
Abstract
Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding large language models (LLMs) in external knowledge, but typical deployments introduce substantial energy, latency, and cost overhead due to expensive retrieval and context-processing pipelines. Recent work in "Green AI" and sustainable machine learning has highlighted the need to treat energy efficiency as a first-class quality attribute alongside accuracy and latency, yet systematic guidance for energy-aware RAG design remains limited. This review synthesizes emerging research on energy-efficient RAG, organizing techniques across four stages of the pipeline: retrieval, reranking, caching, and context management. Building on recent RAG surveys and controlled experiments with production-like systems, we examine empirical evidence showing that adjusting similarity thresholds, reducing embedding dimensionality, applying vector indexing, and using lightweight rerankers can reduce energy consumption by 20–60% in realistic workloads, sometimes without sacrificing accuracy. We also integrate cross-cutting work on token reduction, KV-cache management, semantic caching, green prompt engineering, and phase-level energy analysis for LLM inference. The resulting taxonomy highlights concrete design patterns—adaptive retrieval, redundancy-aware context selection, semantic caching of prompts and responses, and babbling suppression—that jointly reduce compute while preserving grounding fidelity. The paper…
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