arXiv:2604.07590v2 Announce Type: replace-cross Abstract: 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
The proliferation of RAG systems highlights their current limitations in complex, heterogeneous data environments, driving immediate demand for more structured and controllable approaches.
This work directly addresses a critical bottleneck in the real-world application of RAG, moving beyond naive implementations to unlock more reliable and effective AI agentic systems.
The explicit structuring of knowledge and controlled query processing proposed by DCD improves RAG system reliability and performance, especially for multi-step queries and diverse corpora.
- · Enterprises deploying RAG at scale
- · Developers of custom AI agents
- · Information retrieval researchers
- · Generic, 'naive' RAG pipelines
- · Users relying on unstructured data for AI outputs
Improved accuracy and reliability of domain-specific AI applications leveraging RAG.
Accelerated development and adoption of AI agents capable of handling complex tasks in diverse data environments.
Enhanced trust and broader integration of AI systems into critical workflows due to reduced hallucination and increased factual grounding.
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Read at arXiv cs.AI