Prompt Position Optimization for Improving Hallucination Detection and Mitigation in Generative AI
Keywords:
prompt position optimization, hallucination detection, generative AI, large language models, system architecture, governance, robustness, fairness, infrastructure, sustainabilityAbstract
Generative artificial intelligence systems, particularly large language models, have demonstrated remarkable fluency across diverse domains, yet their propensity to produce factually incorrect or contextually unsupported content—commonly termed hallucination—continues to undermine trustworthiness in high-stakes applications. While substantial research has focused on detecting and mitigating hallucinations through post-hoc verification, retrieval augmentation, and fine-tuning strategies, the structural role of prompt positioning within input contexts remains underexplored as a systemic lever for enhancing detection and mitigation. This paper presents a comprehensive systems-oriented analysis of prompt position optimization as a cross-cutting mechanism for improving hallucination safeguards in generative AI. We examine how positional biases, including recency and lost-in-the-middle effects, shape model attention and information fidelity, and argue that dynamic, context-aware placement of verification, calibration, and steering prompts can substantially improve hallucination detection rates while reducing computational overhead. Drawing on architectural, infrastructural, and governance perspectives, the paper situates prompt position optimization within a broader socio-technical framework that encompasses fairness, robustness, deployment scalability, and sustainability. We discuss architectural design patterns that integrate learnable prompt insertion policies alongside continual monitoring pipelines, and evaluate trade-offs between latency, cost, and detection performance. Further, we address governance implications, including transparency in prompt interventions and the need for standardized audit protocols that account for prompt position as a controllable design parameter. By synthesizing insights from prompt engineering, model alignment, and systems engineering, this work establishes prompt position optimization as a foundational component of resilient, accountable generative AI infrastructure and charts pathways for interdisciplinary research and regulatory engagement.
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