DCD: Domain-Oriented Design for Controlled Retrieval-Augmented Generation
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge sources. However, when applied to heterogeneous corpora and multi-step queries, Naive RAG pipelines often degrade in quality due to flat knowledge representations and the absence of explicit workflows. In this work, we introduce DCD (Domain-Collection-Document), a domain-oriented design to structure knowledge and control query processing in RAG systems without modifying the underlying language model. The proposed approach relies on a hierarchical decomposition of the information space and multi-stage routing based on structured model outputs, enabling progressive restriction of both retrieval and generation scopes. The architecture is complemented by smart chunking, hybrid retrieval, and integrated validation and generation guardrail mechanisms. We describe the DCD architecture and workflow and discuss evaluation results on synthetic evaluation dataset, highlighting their impact on robustness, factual accuracy, and answer relevance in applied RAG scenarios.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 17 of its 21 references that are in the map. First posted on arXiv 8 Apr 2026.
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17 of 21 references.
References · 17
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- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
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- 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
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- 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
- 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
- 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
- 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMscited by 40
- 2023Drilling Down into the Discourse Structure with LLMs for Long Document Question Answeringcited by 8
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- 2025DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization
- 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
- 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
- 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
- 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
- 2023Retrieval-Augmented Generation for Large Language Models: A Survey
- 2024Meta Knowledge for Retrieval Augmented Large Language Models
- 2025Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device