
This paper introduces a novel dual-mechanism approach for achieving fine-grained emotional control in Large Language Model (LLM) prompt engineering. The system combines discrete word substitution with continuous capitalization modulation, enabling significantly more granular emotional expression than traditional single-mechanism approaches. We present the theoretical framework, demonstrate practical applications in multi-agent AI governance systems, and discuss implications for human-AI interaction design.
LLM prompt engineering, emotion tagging, word substitution, multi-agent systems, capitalization modulation, AI governance
LLM prompt engineering, emotion tagging, word substitution, multi-agent systems, capitalization modulation, AI governance
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