Adaptive RAG Framework for Reducing LLM Hallucinations

Large Language Models have shown considerable ability in natural language understanding and text generation. Their tendency to hallucinate, that is, to produce inaccurate, misleading, or unsupported information, nevertheless remains a major obstacle to reliability. RAG eases this problem by bringing external knowledge sources into response generation, but conventional systems can still hallucinate because of irrelevant retrieval, insufficient evidence, outdated information, and misinterpretation of the retrieved context. This research proposes an Adaptive RAG framework that combines query complexity analysis, adaptive retrieval strategy selection, document relevance evaluation, context refinement, response generation, and evidence-based response verification. By adjusting retrieval to the needs of each query, the approach aims to improve the quality of retrieved information and avoid unnecessary retrieval. In addition, the verification mechanism is designed to detect unsupported claims and, when evidence is lacking, trigger further retrieval or communicate uncertainty. Several directions could extend this work: ●     Advanced query classification:  Machine-learning-based classifiers could select retrieval strategies more accurately, helping the system tell apart simple factual questions, complex multi-step queries, and questions that need additional external evidence. ●     Hybrid retrieval and source quality:  Combining keyword-based search with semantic vector search could improve retrieval across query types, while document credibility assessment, source verification, and timestamp-based filtering may reduce the influence of unreliable or outdated information. ●     Claim-level verification:  Individual statements in a response could be compared with the retrieved evidence, supported by automated citation generation and better uncertainty estimation to show when the evidence is too weak for a reliable answer. ●     Computational efficiency:  Adaptive stopping criteria, efficient document reranking, and controlled retrieval iterations could cut response latency and resource consumption with little effect on answer quality. ●     Broader evaluation:  Experiments across multiple datasets, language models, and application domains, together with ablation studies and human assessment, could reveal which components contribute most to hallucination reduction and how well the framework generalizes.

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