arXiv:2605.27445v1 Announce Type: cross Abstract: Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. In this work, we propose a modular framework for benchmarking and guiding the efficient development of RAG applications by focusing on resource telemetry and component recommendation, suggesting the best components for a domain-specific dataset. Our approach leverages core techniques in L
The rapid deployment and scaling challenges of LLM applications, particularly RAG, necessitate more efficient and effective development and evaluation frameworks.
This framework offers a structured approach to optimize RAG deployments, addressing computational demands and knowledge base issues, which will accelerate the broader adoption of LLM-based applications.
The development and evaluation of RAG applications become more systematic and resource-efficient, leading to more reliable and performant LLM deployments.
- · AI developers
- · Enterprises deploying LLMs
- · Cloud providers
- · Inefficient RAG pipelines
- · Manual tuning processes
More robust and scalable Retrieval-Augmented Generation applications will be developed and deployed.
This efficiency gain could lower barriers to entry for custom AI solutions across various industries.
Improved RAG performance might accelerate the obsolescence of older AI systems that lack dynamic knowledge integration.
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Read at arXiv cs.AI