DCD: Domain-Oriented Design for Controlled Retrieval-Augmented Generation
Researchers introduced DCD (Domain-Collection-Document), a hierarchical architecture designed to improve Retrieval-Augmented Generation (RAG) systems when handling heterogeneous data and complex queries. The approach structures knowledge through domain-oriented decomposition and multi-stage routing without modifying underlying language models. This work addresses a key limitation in current RAG systems: degraded performance on diverse corpora and multi-step queries.
The paper presents DCD, a domain-oriented design framework that improves how Retrieval-Augmented Generation systems process information from diverse knowledge sources. Traditional RAG pipelines struggle with heterogeneous corpora and multi-step queries due to flat knowledge representations and lack of explicit workflows. DCD addresses this through hierarchical decomposition of the information space, multi-stage routing based on structured model outputs, and progressive restriction of retrieval and generation scopes. The architecture incorporates smart chunking, hybrid retrieval mechanisms, and integrated validation and generation guardrails. Evaluation on synthetic datasets demonstrated improvements in robustness, factual accuracy, and answer relevance across applied RAG scenarios.
What's missing
The paper evaluates performance on synthetic datasets; evaluation results on real-world heterogeneous corpora and comparison with other domain-oriented RAG approaches would strengthen the empirical validation. Additionally, computational overhead and scalability characteristics of the multi-stage routing approach are not discussed.
What different sources said
- arXiv cs.AICenter
DCD: Domain-Oriented Design for Controlled Retrieval-Augmented Generation
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