arXiv:2411.19463v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges when making RAG deployment decisions. While existing research prioritizes algorithmic innovations, a systematic gap persists in understanding fundamental engineering trade-offs that determine RAG success. We present the first comprehensive study of three universal RAG deployment decisions: whether to deploy RAG, how much information to retrieve, and how to inte
The rapid adoption of Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs) has created an urgent need for systematic understanding of its implementation, moving beyond algorithmic novelties.
This research provides a foundational framework for practitioners and developers to make informed RAG deployment decisions, directly impacting the efficacy and widespread application of LLMs in real-world systems.
The focus in RAG development is shifting from purely theoretical algorithmic advances to practical engineering trade-offs and deployment strategies, improving the reliability and performance of AI applications.
- · AI developers
- · Enterprises deploying LLMs
- · AI platforms
- · Data scientists
- · Inefficient RAG implementations
- · Companies with suboptimal LLM deployments
Improved performance and reliability of enterprise-level AI applications leveraging Retrieval-Augmented Generation.
Increased trust and adoption of LLM-based solutions in critical business processes due to better deployment practices.
The acceleration of AI agent development as RAG systems become more robust, forming more reliable foundations for autonomous systems.
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Read at arXiv cs.AI