LLM prompt optimization and design patterns. Use for crafting effective prompts, chain-of-thought, and AI integration.
Scanned 9/8/2026
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---
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
```markdown
# 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
- [ ] Define clear role/persona
- [ ] Provide sufficient context
- [ ] Be specific about output format
- [ ] Use examples when helpful
- [ ] Iterate and refine prompts
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