RAGBoost: Efficient Retrieval-Augmented Generation with Accuracy-Preserving Context Reuse

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with retrieved context but often suffers from downgraded prefill performance as modern applications demand longer and more complex inputs. Existing caching techniques either preserve accuracy with low cache reuse or improve reuse at the cost of degraded reasoning quality. We present RAGBoost, an efficient RAG system that achieves high cache reuse without sacrificing accuracy through accuracy-preserving context reuse. RAGBoost detects overlapping retrieved items across concurrent sessions and multi-turn interactions, using efficient context indexing, ordering, and de-duplication to maximize reuse, while lightweight contextual hints maintain reasoning fidelity. It integrates seamlessly with existing inference engines (SGLang and vLLM) and improves performance by 1.5–3× over state-of-the-art methods (CacheBlend, RadixCache, LMCache, HiCache, and RAGCache), while preserving or even enhancing reasoning accuracy across diverse RAG and agentic AI workloads.