A Systematic Review of Retrieval-Augmented Generation Techniques for Mitigating Hallucinations in Chatbot Systems
Abstract
Large language model (LLM) chatbots are increasingly deployed in education, health care, and decision-support settings, yet their reliability is limited by hallucination: fluent responses that are factually incorrect, weakly grounded, or inconsistent with supplied evidence. Retrieval-Augmented Generation (RAG) addresses this limitation by conditioning generation on external knowledge retrieved at inference time. This paper presents a systematic review of RAG techniques for hallucination mitigation in chatbot systems. Following the PRISMA 2020 process, studies published from 2020 to 2026 were searched in Scopus, IEEE Xplore, and Google Scholar. After duplicate removal, screening, and full-text eligibility assessment, 23 studies were synthesized qualitatively. The review analyzes RAG architectures, retrieval strategies, evidence-integration mechanisms, and evaluation practices. Findings show that advanced and modular RAG architectures generally improve factuality and faithfulness over purely parametric LLMs, especially when retrieval is paired with re-ranking, evidence filtering, verification, or citation-aware generation. However, mitigation remains constrained by retrieval noise, latency, limited benchmark standardization, and weak integration between hallucination detection and correction. The paper concludes with research directions for trustworthy, efficient, and explainable RAG-based chatbot systems.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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