Reducing Hallucinations in Large Language Models Through External Knowledge Retrieval

Ansh Mittal

Reducing Hallucinations in Large Language Models Through External Knowledge Retrieval

Author(s)	Mr. Ansh Mittal
Country	India
Abstract	Large Language Models (LLMs) have rapidly transformed conversational artificial intelligence by enabling systems to generate fluent, contextually relevant, and human-like responses across a wide range of applications. However, their increasing use in information-intensive domains has highlighted the persistent problem of hallucination, in which models generate inaccurate, unsupported, or fabricated information. This issue is particularly significant in educational and career guidance, where incorrect information concerning university admissions, scholarships, visa regulations, immigration pathways, and career opportunities can influence important student decisions. This secondary review examines the causes of hallucinations in LLMs and evaluates existing approaches for improving factual reliability, with particular emphasis on external knowledge retrieval and Retrieval-Augmented Generation (RAG). The review discusses fine-tuning, Reinforcement Learning from Human Feedback, prompt engineering, semantic search, embedding models, vector databases, knowledge graphs, and hybrid retrieval architectures. It further examines evaluation approaches based on retrieval performance, factual correctness, relevance, faithfulness, and human assessment. The literature indicates that retrieval-based approaches can reduce dependence on the static knowledge encoded within LLM parameters by connecting models with external and potentially updated information sources. However, retrieval does not completely eliminate hallucinations, as response reliability remains dependent on the quality, relevance, completeness, and recency of retrieved information. Based on these findings, the paper proposes a conceptual framework for a domain-specific conversational assistant integrating curated educational and immigration data, document processing, semantic retrieval, vector databases, LLM-based generation, and source-aware user interaction. The review concludes that combining language generation with reliable external knowledge represents a promising direction for developing more accurate, transparent, and trustworthy AI systems for higher education and career decision-making.
Keywords	Large Language Models, hallucination, Retrieval-Augmented Generation, external knowledge retrieval, semantic search, vector databases, knowledge graphs, generative AI, educational technology, conversational AI
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-28

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Reducing Hallucinations in Large Language Models Through External Knowledge Retrieval

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