Designing Culturally and Spiritually Sensitive AI Assistants for High-Density Religious Environments: A Prompt Engineering Framework Applied at Masjid al-Haram

Masjid al-Haram in Makkah, Saudi Arabia, receives millions of pilgrims annually from over 180 countries, presenting distinctive challenges in emergency response and pilgrim assistance within a setting of profound religious significance. This study applies prompt engineering principles to design, build, and test two conversational AI assistants for this environment: Noor, a lost pilgrim assistant, and Shifa, a medical emergency triage assistant. Both assistants were implemented using persona design, priority ordering, constraint setting, and few-shot logic and were iteratively tested on the VAPI voice AI platform using simulated pilgrim interactions. From this practice-based work, the SACRED Framework is proposed, a six-principle design guide for AI practitioners operating in culturally and religiously sensitive environments, covering Sensitivity, Accessibility, Clarity, Reliability, Escalation, and Debugging. Two case studies report the design decisions, bugs identified and resolved, and testing observations for each assistant. The findings indicate that prompt engineering, applied systematically and iteratively, can produce conversational AI assistants that are culturally respectful, behaviorally reliable, and appropriately scoped for high-stakes deployment in sacred spaces. Concrete limitations, including English-only language support and the absence of formal deployment oversight, are identified and treated as motivation for follow-up empirical work.