(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The rapid proliferation of large-scale transformer architectures has driven an urgent need for parameter-efficient fine-tuning (PEFT) methods that can adapt pre-trained models to diverse downstream tasks without incurring prohibitive computational, memory, and carbon costs. Within the PEFT landscape, prompt-based strategies have emerged as a lightweight alternative to full model updating, yet their effectiveness remains tightly coupled to the placement of soft prompts within the network topology. This paper presents a comprehensive system-level analysis of hierarchical prompt placement strategies, conceptualizing the design space as a multi-scale governance problem that balances expressiveness, modularity, and efficiency. We argue that the strategic allocation of learnable prompt parameters across transformer layers, attention heads, and feed-forward blocks constitutes a form of architectural policy-making, with far-reaching implications for robustness, fairness, sustainability, and deployment logistics. Drawing upon insights from adapter-based methods, prefix tuning, low-rank adaptation, and selective insertion techniques, we develop a taxonomy of placement policies ranging from static layer-wise injection to dynamic, context-dependent routing. The discussion extends beyond accuracy metrics to examine how hierarchical placement influences bias amplification, cross-task interference, energy consumption, and model auditability in real-world infrastructure. By reframing prompt placement as a system-level resource allocation challenge, this work bridges the gap between algorithmic innovation and the socio-technical demands of large-scale AI systems. We conclude with forward-looking perspectives on governance frameworks for PEFT deployment, emphasizing the necessity of transparency, reproducibility, and lifecycle accounting in the design of efficient adaptation mechanisms.