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.

Research-backed prompting techniques for improved AI response quality (+45-115% improvement). Use when optimizing prompts, enhancing agent instructions, or when maximum response quality is critical. Invoked by /ai-eng/optimize command. Includes expert persona, stakes language, step-by-step reasoning, challenge framing, and self-evaluation techniques. Source: v1truv1us/ai-eng-system.

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