incentive-prompting

SKILL.md

Using proper prompting techniques is critical to achieving optimal AI output quality. Research shows these techniques can improve response quality by 45-115%. The difference between a mediocre AI response and an excellent one often comes down to prompt engineering. Whether you're optimizing agents, enhancing commands, or working on complex problems, applying these techniques consistently yields significantly bette...

Research-backed techniques that leverage statistical pattern-matching to elicit higher-quality AI responses. Based on peer-reviewed research from MBZUAI (Bsharat et al.), Google DeepMind (Yang et al.), and ICLR 2024 (Li et al.).

LLMs don't understand incentives, but they pattern-match on language associated with high-effort training examples. Stakes language triggers selection from distributions of higher-quality text patterns.

研究支持的提示技术可提高 AI 响应质量（提高 45-115%）。在优化提示、增强代理指令或最大响应质量至关重要时使用。由 /ai-eng/optimize 命令调用。包括专家角色、风险语言、分步推理、挑战框架和自我评估技术。 来源：v1truv1us/ai-eng-system。

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