majiayu000 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
- Role & Goal: Who is Claude? What is the objective?
- Context/Data: Reference materials wrapped in XML.
- Rules & Constraints: Dos and Don'ts.
- Examples (Few-Shot): Golden samples.
- Instruction: The immediate task.
- 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?