Enhancing LLMs with Context-Specific Knowledge for Mitigating Misinformation in SMEs: A RAG-based Modeling and Analysis
06:00 · August 4, 2026 · arXiv cs.AI RSS
Large Language Models (LLMs), a part of artificial intelligence (AI), are increasingly being adopted by Small and Medium Enterprises (SMEs) to enhance question-answering capabilities and support business decision-making processes. However, hallucinations in LLM-generated outputs can serve as a source of misinformation, reducing user confidence in their reliability and trustworthiness within SMEs. Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address this challenge by incorporating external knowledge sources into the modeling process. In this paper, we present VectorRAG and GraphRAG modeling approaches to mitigate hallucinations and misinformation risks and evaluate their effectiveness in SME environments. Our experimental evaluation is conducted on multiple state-of-the-art LLMs, including LLaMA, Mistral, and Qwen, to assess performance in terms of useful response generation, risk of hallucination, contextual relevance, as well as human-interpretation. The results demonstrate that RAG-enhanced LLMs can significantly improve response quality by reducing hallucinations and misinformation, thereby supporting more reliable, trustworthy, and context-aware decision-making in SME environments.
Summary
SMEs frequently adopt large language models to support question-answering and decision-making, yet their limited technical resources leave them exposed to hallucinations that inject misinformation, particularly in specialized areas such as cybersecurity. The study examines two retrieval-augmented generation approaches designed to anchor model outputs in external knowledge: VectorRAG, which retrieves text passages through semantic similarity search, and GraphRAG, which organizes information into knowledge graphs to expose explicit relationships among entities.
Experiments were conducted on a dataset of cybersecurity documents using several contemporary models, including LLaMA, Mistral, and Qwen. Performance was assessed across dimensions of useful response generation, hallucination risk, contextual relevance, and human interpretability. In this domain, VectorRAG consistently delivered higher contextual relevance and completeness than GraphRAG, although both methods reduced unsupported claims relative to unaugmented baselines.
The authors position the comparison within a broader modeling framework intended to guide the responsible integration of retrieval-augmented systems in resource-constrained business settings. By grounding generation in curated external sources, the approach aims to improve factual consistency and support more trustworthy AI-assisted decisions without requiring extensive in-house infrastructure.
Why it matters
The research directly addresses the challenge of deploying trustworthy and hallucination-free AI in SMEs, a major focus of the Dutch AI ecosystem. The comparative analysis of RAG methodologies offers actionable insights for Dutch researchers and developers building compliant, reliable AI solutions aligned with EU ethical standards.
