Retrieval-Augmented Generation for Domain-Specific Intelligent Decision Support Systems
Keywords:
retrieval-augmented generation, decision support systems, domain adaptation, knowledge integration, robustness, fairness, governance, deployment sustainabilityAbstract
The integration of large language models into decision support systems has opened new possibilities for automated reasoning and natural language interaction. However, standalone generative models often suffer from hallucination, outdated knowledge, and insufficient domain specificity. Retrieval-augmented generation (RAG) addresses these limitations by coupling a neural retriever with a generative component, enabling systems to ground responses in external, updatable knowledge bases. This paper presents a comprehensive examination of RAG in the context of domain-specific intelligent decision support systems, spanning architecture, knowledge integration, robustness, fairness, governance, deployment, and sustainability. We argue that the effectiveness of RAG-based decision support depends critically on the design of the retrieval pipeline, the quality and provenance of domain corpora, and the alignment of outputs with domain-specific reasoning requirements. Through analytical discussion and illustrative case studies from medicine, law, and engineering, we highlight structural trade-offs between retrieval latency and answer faithfulness, between coverage and precision in knowledge bases, and between generative flexibility and regulatory compliance. We also consider the socio-technical implications of deploying such systems in high-stakes environments, including biases propagated through retrieved documents, the need for transparent audit trails, and the long-term sustainability of knowledge curation efforts. The paper concludes with forward-looking perspectives on multimodal RAG, interactive decision loops, and the evolving policy landscape. Our analysis aims to provide researchers and practitioners with a system-level understanding of how to design, evaluate, and govern RAG-based decision support systems that are both intelligent and trustworthy.
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