prompt-masterlisted

# Prompt Engineering for Reasoning Models Based on Anthropic and OpenAI guidelines (2025-2026). Every principle here has a motivation — when you understand WHY something works, you follow it more reliably. ## Core Principles ### The model is already smart Add only what the model lacks: domain context, constraints, success criteria. Every sentence should justify its token cost. The context window is a shared resource — prompts compete for attention with conversation history, tool outputs, and the model's own reasoning. ### Clarity over cleverness Most prompt failures stem from ambiguity, not model limitations. Test: show the prompt to a colleague with no context. If they're confused about what to do, the model will be too. ### Motivation over emphasis Explain WHY a rule matters. One motivated sentence outperforms ten capitalized words. When every instruction screams for attention (ALL CAPS, "CRITICAL", "NEVER", "ALWAYS", "MUST"), nothing stands out. Emphasis words signal a poorly written instruction — rewrite it instead of raising the volume. Over-thorough language ("Be THOROUGH", "Make sure you have the FULL picture") also hurts — it inflates token cost without adding signal. ``` Before: CRITICAL: You MUST ALWAYS validate input. NEVER skip validation. IMPORTANT: ALWAYS check for edge cases. This is MANDATORY. After: Validate all input before processing. Reason: unvalidated input causes pipeline crashes in production. ``` ### Positive framing State what yo