Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG
Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost challenges in enterprise environments due to a persistent knowledge gap. This paper envisions trustworthy, scalable, and cost-efficient integration through knowledge-grounded LLMs and agents operating within a retrieval-augmented generation (RAG) workflow. Here, trustworthiness refers to evidence-grounded, verifiable reasoning, where integration decisions are transparently supported by retrieved knowledge, robust against hallucination, and consistent across tasks. We trace the evolution from clas...
| Authors | Chuangtao Ma, Arijit Khan |
|---|---|
| Primary category | cs.DB |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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