<![CDATA[The increasing deployment of large language models (LLMs) in enterprise environments creates significant reliability challenges, including hallucination, factual inconsistency, limited knowledge traceability, uncertainty, and operational inefficiency. This study develops a literature-based architectural framework for reliable knowledge retrieval systems that separates external knowledge management from LLM-based reasoning and generation. The framework synthesizes key architectural mechanisms, including knowledge representation, hybrid retrieval, reranking, evidence selection, context construction, response verification, provenance tracking, uncertainty handling, guardrails, and computational efficiency. The resulting architecture organizes these mechanisms into coordinated layers that regulate the flow of external evidence from knowledge sources to generated responses while supporting traceability, evidence grounding, and controlled abstention when sufficient evidence is unavailable. The architectural synthesis further identifies complementary strategies for enterprise deployment, including semantic coaching, model routing, and human oversight, to balance reliability, scalability, responsiveness, and operational cost. The analysis indicates that reliable LLM deployment should be addressed as an end-to-end architectural challenge rather than solely as a model-performance problem. In this perspective, knowledge access, evidence quality, retrieval accuracy, verification, provenance, uncertainty management, and governance must function as integrated system components. The proposed framework provides a structured foundation for designing maintainable, auditable, reliable knowledge retrieval systems capable of supporting enterprise applications and other high-stakes environments. By explicitly separating knowledge acquisition, retrieval, reasoning, verification, and governance functions, the framework also facilitates modular implementation, systematic evaluation, and continuous improvement. Consequently, it offers practical architectural guidance for organizations seeking to deploy LLM-based systems while maintaining factual reliability, operational control, transparency, and accountability across evolving enterprise knowledge environments]]>
