name: prompt-engineering description: Designs and optimizes prompts for large language models to achieve better, more consistent outputs. Trigger keywords: prompt, LLM, GPT, Claude, prompt engineering, AI prompts, few-shot, chain of thought. allowed-tools: Read, Grep, Glob, Edit, Write

Prompt Engineering

Overview

This skill focuses on crafting effective prompts for large language models. It covers techniques for improving output quality, consistency, and reliability across various use cases.

Instructions

1. Define the Task Clearly

Identify the specific goal
Determine output format requirements
Consider edge cases
Plan for error handling

2. Structure the Prompt

Use clear, specific instructions
Provide relevant context
Include examples when helpful
Specify constraints and format

3. Apply Techniques

Chain of thought reasoning
Few-shot learning
Role prompting
Output formatting

4. Iterate and Refine

Test with diverse inputs
Analyze failure cases
Optimize for consistency
Document effective patterns

Best Practices

Be Specific: Vague prompts yield vague results
Provide Context: Give necessary background information
Show Examples: Demonstrate desired output format
Constrain Output: Specify format, length, style
Think Step by Step: Break complex tasks into steps
Test Edge Cases: Verify behavior with unusual inputs
Version Control: Track prompt iterations

Examples

Example 1: Basic Prompt Structure

# Poor Prompt

Summarize this article.

# Good Prompt

You are an expert technical writer. Summarize the following article for a software engineering audience.

## Requirements:

- Length: 2-3 paragraphs
- Include: key findings, methodology, and practical implications
- Tone: professional and objective
- Format: plain text with no bullet points

## Article:

{article_text}

## Summary:

Example 2: Few-Shot Learning

# Task: Extract structured data from product descriptions

## Examples:

Input: "Apple MacBook Pro 14-inch with M3 chip, 16GB RAM, 512GB SSD. Space Gray. $1,999"
Output:

```json
{
  "brand": "Apple",
  "product": "MacBook Pro",
  "specs": {
    "screen_size": "14-inch",
    "processor": "M3 chip",
    "ram": "16GB",
    "storage": "512GB SSD"
  },
  "color": "Space Gray",
  "price": 1999
}
```

Input: "Samsung Galaxy S24 Ultra, 256GB, Titanium Black, unlocked - $1,299.99" Output:

{
  "brand": "Samsung",
  "product": "Galaxy S24 Ultra",
  "specs": {
    "storage": "256GB",
    "carrier": "unlocked"
  },
  "color": "Titanium Black",
  "price": 1299.99
}

Now extract data from: Input: "{new_product_description}" Output:


### Example 3: Chain of Thought Prompting
```markdown
# Task: Solve complex reasoning problems

You are a logical reasoning expert. Solve the following problem step by step.

## Problem:
A store sells apples and oranges. Apples cost $2 each and oranges cost $3 each.
If Sarah buys 12 pieces of fruit for exactly $30, how many of each did she buy?

## Solution Process:
Let me work through this systematically:

Step 1: Define variables
- Let a = number of apples
- Let o = number of oranges

Step 2: Set up equations from the constraints
- Total fruit: a + o = 12
- Total cost: 2a + 3o = 30

Step 3: Solve the system
- From equation 1: a = 12 - o
- Substitute into equation 2: 2(12 - o) + 3o = 30
- Simplify: 24 - 2o + 3o = 30
- Solve: o = 6

Step 4: Find remaining variable
- a = 12 - 6 = 6

Step 5: Verify
- 6 apples + 6 oranges = 12 fruit ✓
- 6($2) + 6($3) = $12 + $18 = $30 ✓

## Answer:
Sarah bought 6 apples and 6 oranges.

Example 4: System Prompt for Code Generation

# System Prompt for Code Assistant

You are an expert software engineer assistant. When writing code:

## Code Quality Standards:

1. Write clean, readable code with meaningful variable names
2. Include comprehensive error handling
3. Add type hints (Python) or TypeScript types
4. Follow language-specific conventions (PEP 8 for Python, ESLint for JS)
5. Include docstrings/JSDoc for public functions

## Response Format:

1. First, briefly explain your approach (2-3 sentences)
2. Then provide the code implementation
3. Finally, explain any important design decisions or trade-offs

## Constraints:

- Prefer standard library solutions over external dependencies
- Optimize for readability over cleverness
- Include input validation for public APIs
- Write testable code with dependency injection where appropriate

## When Uncertain:

- Ask clarifying questions before implementing
- State assumptions explicitly
- Offer alternative approaches if applicable

---

User: Write a function to parse and validate email addresses

Example 5: Output Formatting Control

# Task: Analyze sentiment with structured output

Analyze the sentiment of the following customer reviews. For each review, provide:

1. Sentiment classification (positive/negative/neutral)
2. Confidence score (0.0 to 1.0)
3. Key phrases that indicate the sentiment
4. Suggested response action

## Output Format (JSON):

```json
{
  "reviews": [
    {
      "id": 1,
      "text": "original review text",
      "sentiment": "positive|negative|neutral",
      "confidence": 0.95,
      "key_phrases": ["phrase1", "phrase2"],
      "action": "thank|apologize|follow_up|escalate"
    }
  ],
  "summary": {
    "total": 3,
    "positive": 1,
    "negative": 1,
    "neutral": 1,
    "average_confidence": 0.85
  }
}
```

Reviews to Analyze:

{reviews_list}

prompt-engineering

Install Skill

SKILL.md