AMulti-Stage Hallucination Mitigation Framework for Reliable Retrieval-Augmented Generation Systems

Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. However, despite their effectiveness, LLMs frequently suffer from hallucinations, generating information that appears plausible but lacks factual grounding. Retrieval-Augmented Generation (RAG) systems address this limitation by incorporating external knowledge during response generation. Nevertheless, existing RAG systems remain vulner able to hallucinations due to irrelevant retrieval, incomplete contextual evidence, unsupported claim generation, and the absence of reliability assessment mechanisms.This paper proposes a Multi-Stage Hallucination Mitigation Framework for Reliable Retrieval-Augmented Generation Systems that progressively improves answer reliability through query intelligence, adaptive retrieval with web fallback, contextual reranking, claim-level verification, and confidence-based humanescalation. To ensure scientific validity, all experimental versions maintain identical embedding, retrieval, and generation models while only introducing incremental reliability-enhancing mechanisms. Experimental evaluation conducted across six framework versions (V0–V5) demon strates substantial improvements in retrieval relevance, answer relevance, and faithfulness while significantly reducing hallucinated responses. The proposed framework provides a scalable,domain-independent, and deployment-ready architecture suitable for educational institutions,enterprise knowledge systems, healthcare assistants, and customer support applications.