| name | prompt-engineering |
| description | LLM prompt optimization and design patterns. Use for crafting effective prompts, chain-of-thought, and AI integration. |
🎯 Prompt Engineering Skill
Prompt Patterns
Zero-Shot
Translate the following text to Thai: "Hello, how are you?"
Few-Shot
Examples:
Input: "Good morning" → Output: "สวัสดีตอนเช้า"
Input: "Thank you" → Output: "ขอบคุณ"
Input: "Goodbye" → Output: ?
Chain-of-Thought (CoT)
Let's solve this step by step:
1. First, identify the problem
2. Break down the components
3. Apply relevant formula
4. Calculate the answer
5. Verify the result
Prompting Best Practices
Structure
# Role
You are an expert Python developer.
# Context
We are building a REST API using FastAPI.
# Task
Create a user registration endpoint.
# Requirements
- Validate email format
- Hash password with bcrypt
- Return JWT token
# Output Format
Return complete, working code with comments.
Be Specific ✅
❌ "Write code for a button"
✅ "Create a React button component that:
- Has primary and secondary variants
- Accepts onClick handler
- Shows loading spinner when isLoading is true
- Uses Tailwind CSS for styling"
Advanced Techniques
Self-Consistency
Generate 3 different solutions, then pick the best one.
Tree-of-Thought
Consider multiple approaches:
Approach A: [describe]
Approach B: [describe]
Approach C: [describe]
Compare and select optimal solution.
RAG Pattern
Context: [Retrieved documents]
Question: [User query]
Answer based ONLY on the context provided.
Temperature Guide
| Temperature | Use Case |
|---|
| 0.0 - 0.3 | Factual, code generation |
| 0.4 - 0.7 | Balanced creativity |
| 0.8 - 1.0 | Creative writing |
Checklist
