Retrieval-Augmented Generation systems have emerged as a critical solution for mitigating hallucinations in large language models by grounding responses in external knowledge. However, systematic failures in the retrieval pipeline can paradoxically exacerbate hallucination problems rather than solve them. This paper presents a comprehensive analysis of how vector database design choices, approximate nearest neighbor search parameters, and chunking strategies quantitatively impact LLM output quality. Through systematic experimentation across multiple datasets including HotpotQA, Natural Questions, and domain- specific corpora, we demonstrate that retrieval recall rates below 80% correlate with a 35% increase in hallucination incidents. Our findings reveal that pre-filtering strategies in hybrid search systems reduce hallucination rates.retrieved from curated knowledge bases, theoretically grounding model outputs in factual by 42% compared to post-filtering approaches, while semantic chunking outperforms fixedlength segmentation by 28% in factual accuracy metrics. We introduce novel evaluation frameworks combining RAGAS faithfulness scores with traditional metrics, establishing quantitative relationships between retrieval quality and generation reliability. The paper contributes actionable insights for practitioners designing production RAG systems, including adaptive threshold mechanisms and multi-stage retrieval architectures that maintain sub-100ms latency while achieving 95% retrieval recall.