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SAGE system optimizes RAG retrieval for latency and cost · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SAGE, a new adaptive retrieval policy for production Retrieval-Augmented Generation (RAG) systems. SAGE dynamically adjusts the number of passages retrieved per query based on estimated query difficulty, aiming to meet strict service level objectives (SLOs) for latency and cost. The system uses lightweight features from initial retrieval and is trained offline, adding minimal overhead at inference. Experiments show SAGE significantly improves SLO compliance and reduces latency and cost compared to static baselines, while maintaining answer quality across various datasets and LLM families. AI

IMPACT Optimizes RAG systems for production environments by improving latency and cost efficiency, potentially enabling wider adoption.

RANK_REASON The cluster contains a research paper detailing a new method for retrieval-augmented generation systems.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SAGE system optimizes RAG retrieval for latency and cost · 2 sources tracked

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The cluster contains a research paper detailing a new method for retrieval-augmented generation systems.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Muhammad Faizan Raza (Luna), Shuo (Luna), Yang, Satish Mahadevan Srinivasan ·

    SAGE: SLO-Aware Adaptive Retrieval for Production RAG Systems

    arXiv:2608.08237v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems in production operate under strict service level objectives (SLOs) on tail latency and infrastructure cost. However, standard retrieval pipelines rely on fixed retrieval budgets that ig…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Satish Mahadevan Srinivasan ·

    SAGE: SLO-Aware Adaptive Retrieval for Production RAG Systems

    Retrieval-Augmented Generation (RAG) systems in production operate under strict service level objectives (SLOs) on tail latency and infrastructure cost. However, standard retrieval pipelines rely on fixed retrieval budgets that ignore query difficulty, over-retrieving for easy qu…

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