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Navigating specialty care often requires patients to understand referral reasons, appointment logistics, preparation rules, insurance requirements, and follow-up expectations. These instructions are frequently distributed across separate documents and portals, creating avoidable confusion for patients and caregivers. No unified system currently converts fragmented referral, clinic, insurance, preparation, and scheduling information into one personalized, plain-language care navigation guide. As a result, patients may miss critical steps before appointments or misunderstand what they need to do. This article proposes a conceptual large language model system for generating patient-friendly care navigation instructions from clinical, administrative, and scheduling data. The objective is to describe how such a system could support clearer, safer, and more accessible patient communication. The proposed pipeline would extract relevant facts from referral orders, clinic requirements, insurance rules, preparation instructions, and scheduling constraints. A retrieval-augmented LLM would then synthesize these facts into a cohesive instruction sheet with traceability back to verified institutional sources. Conceptually, the system would generate a clear, step-by-step appointment guide tailored to the patient’s language, health literacy needs, and preferred communication channel. The output would be expected to reduce cognitive burden by consolidating complex healthcare logistics into one practical message. An LLM-based patient navigation instruction system could bridge the communication gap between healthcare operations and patient understanding. Responsible deployment would require strong grounding, validation, accessibility design, and human oversight for high-risk instructions.