prompt-optimization

# Prompt Optimization

This skill optimizes prompts for LLMs and AI systems, focusing on effective prompt patterns, few-shot learning, and optimal AI interactions.

## When to Use This Skill

- When building AI features or agents
- When improving LLM response quality
- When crafting system prompts
- When optimizing agent performance
- When implementing few-shot learning
- When designing AI workflows

## What This Skill Does

1. **Prompt Design**: Creates effective prompts with clear structure
2. **Few-Shot Learning**: Implements few-shot examples for better results
3. **Chain-of-Thought**: Uses reasoning patterns for complex tasks
4. **Output Formatting**: Specifies clear output formats
5. **Constraint Setting**: Sets boundaries and constraints
6. **Performance Optimization**: Improves prompt efficiency and results

## How to Use

### Optimize Prompt

```
Optimize this prompt for better results
```

```
Create a system prompt for a code review agent
```

### Specific Patterns

```
Implement few-shot learning for this task
```

## Prompt Techniques

### Structure

**Clear Sections:**

- Role definition
- Task description
- Constraints and boundaries
- Output format
- Examples

### Few-Shot Learning

**Pattern:**

- Provide 2-3 examples
- Show input-output pairs
- Demonstrate desired style
- Include edge cases

### Chain-of-Thought

**Approach:**

- Break down complex tasks
- Show reasoning steps
- Encourage step-by-step thinking
- Verify intermediate results

## Examples

### Example 1: Code Review Prompt

**Input**: Create optimized code review prompt

**Output**:

```markdown
## Optimized Prompt: Code Review

### The Prompt
```

You are an expert code reviewer with 10+ years of experience.

Review the provided code focusing on:

1. Security vulnerabilities
2. Performance optimizations
3. Code maintainability
4. Best practices

For each issue found, provide:

- Severity level (Critical/High/Medium/Low)
- Specific line numbers
- Explanation of the issue
- Suggested fix with code example

Format your response as a structured report with clear sections.

```

### Techniques Used
- Role-playing for expertise
- Clear evaluation criteria
- Specific output format
- Actionable feedback requirements
```

## Best Practices

### Prompt Design

1. **Be Specific**: Clear, unambiguous instructions
2. **Provide Examples**: Show desired output format
3. **Set Constraints**: Define boundaries clearly
4. **Iterate**: Test and refine prompts
5. **Document**: Keep track of effective patterns

## Related Use Cases

- AI agent development
- LLM optimization
- System prompt creation
- Few-shot learning implementation
- AI workflow design