Measuring Retrieval Freshness and Accuracy Degradation in Continuous ETL‑Driven RAG Systems

Abstract

Retrieval‑augmented generation systems have transformed enterprise deployments by grounding large language model outputs in external documents, yet their effectiveness degrades significantly as underlying corpora evolve through continuous ETL pipelines and streaming ingestion. The CRAG benchmark reveals fundamental performance gaps where even optimized industry-grade RAG architectures fail substantial portions of temporally sensitive queries, while the HOH benchmark demonstrates that outdated information causes catastrophic accuracy losses and can render retrieval augmentation counterproductive compared to standalone language models. RAGBench enables component‑level decomposition, separating retrieval quality from generation fidelity across extensive labeled examples, while Streaming RAG establishes that incremental real‑time updates yield measurable recall improvements with production‑grade latency and throughput characteristics. Dense vector retrievers have an issue in maintaining fresh embeddings, which hybrid architectures between semantic and lexical signals have shown to be more resilient in the face of staleness. Empirical evidence across benchmarks confirms that retrieval freshness constitutes a first‑order determinant of end‑to‑end accuracy and hallucination behavior, with degradation effects sufficiently severe to eliminate or reverse the value proposition of retrieval augmentation under conditions of substantial staleness. These results prove that companies that use RAG in dynamic settings should introduce cost-sensitive and domain-aware update methods that balance between computational costs and accuracy demands. The transition from ad hoc refresh policies to principled freshness management becomes essential for maintaining reliable performance as retrieval indices diverge from evolving source systems.