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name: dev-prompt-engineering description: "Expert guide for crafting high-performance Claude prompts, based on Anthropic's interactive tutorial."

Prompt Engineering (Dev Prompt Engineering)

Core Principles (Anthropic Best Practices)

1. The "Context-First" Rule

• Context: Always provide relevant context before the instruction.
• Role: Assign a persona (e.g., "You are an expert Python architect").
• XML Tags: Use XML tags (e.g., <documents>, <instruction>) to structure input. Claude loves XML.

2. The Power of Examples (Few-Shot)

• Show, Don't Just Tell.
• Provide 3+ examples of "Input -> Ideal Output" to guide style and format.
• Anti-Hallucination: Include examples of how to say "I don't know" or handle edge cases.

3. Precognition (Chain of Thought)

• Let Claude Think: For complex tasks, ask Claude to "Think step-by-step" before answering.
• Thinking Tags: Use <thinking> blocks to verify logic before generating the final <answer>.

4. Language Strategy (Performance vs Usability)

• Prompt Language: English. (LLMs reason better in English). All instructions, constraints, and system prompts must be in English.
• Output Language: Korean. The final response meant for the user must be in Korean.
• Rule: "Think in English, Speak in Korean."

🏗️ Structure of a Great Prompt

1. Role & Goal: Who is Claude? What is the objective?
2. Context/Data: Reference materials wrapped in XML.
3. Rules & Constraints: Dos and Don'ts.
4. Examples (Few-Shot): Golden samples.
5. Instruction: The immediate task.
6. Pre-computation: "Take a deep breath and think step by step..."

✅ Quality Standards

• Clarity: Unambiguous instructions.
• Separation: Data and instructions are visually distinct (XML).
• Iterative: Every prompt should be tested and refined.

Checklist

• Persona: Is a specific role assigned?
• XML Structuring: Are data parts wrapped in tags?
• Examples: Are there at least 2-3 examples?
• CoT: Is identifying the reasoning process (Thinking) required?