Agentic RAG with Human-in-the-Retrieval

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Abstract
Retrieval-Augmented Generation (RAG) has emerged as a promising solution to address key challenges faced by GenAI, such as hallucination, outdated or non-removable parametric knowledge, and non-traceable reasoning processes. Existing RAG frameworks introduce dynamism into RAG process through adaptive, recursive and interactive usage of retriever and generator. More recently, agentic RAG adds another layer of intelligence to RAG by leveraging GenAI agents to further enhance dynamism by autonomously planning the retrieval process as a complex orchestration workflow with various external tools. However, current RAG architectures often overlook the significant role that domain experts can play in the retrieval process, alongside passive knowledge bases. This paper introduces a new paradigm for agentic RAG systems, capable of integrating external passive knowledge bases as well as active domain experts. This integration further enhances the versatility and factual accuracy of RAG systems. The paper discusses the key components of this new paradigm and examines the associated design challenges.

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