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.

研究支持的提示技術可提高 AI 響應質量（提高 45-115%）。在優化提示、增強代理指令或最大響應質量至關重要時使用。由 /ai-eng/optimize 命令調用。包括專家角色、風險語言、分步推理、挑戰框架和自我評估技術。 來源：v1truv1us/ai-eng-system。

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