arXiv:2606.02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency. Static retrieval configurations cannot resolve this tension across heterogeneous query workloads -- simple definitional queries waste budget on unnecessary context, while complex analytical prompts are underserved by shallow retrieval. This paper introduces \emph{Cost-Aware RAG} (CA-RAG), a per-query routing framework that selects from a discrete catalog of \emph{strategy bundle
The proliferation of RAG systems highlights increasing token costs and latency as critical bottlenecks, making cost-aware optimization a timely concern for practical AI deployment.
This development allows for more efficient and cost-effective deployment of advanced AI, directly impacting the economic viability and scalability of AI-driven applications.
AI systems can now dynamically adjust retrieval depth based on query complexity and cost, moving beyond static configurations that either waste resources or provide insufficient context.
- · Companies deploying RAG-based AI systems
- · Cloud providers offering AI services
- · AI researchers focused on efficiency
- · Providers of inefficient RAG solutions
- · Organizations with high, unanticipated AI operational costs
Reduced operational costs for AI applications and improved user experience due to lower latency.
Increased adoption of complex RAG systems across various industries as economic barriers are lowered.
The development of more sophisticated, self-optimizing AI agents capable of managing their own resource consumption.
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Read at arXiv cs.AI