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name: prompt-engineer description: Optimize prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts.

Prompt Engineering

Craft effective prompts for LLM applications.

When to Use

• Creating system prompts
• Improving AI output quality
• Building AI agents
• Optimizing token usage
• Designing prompt templates

Core Techniques

Role Setting

CopyYou are an expert [role] with [X] years of experience in [domain].
Your task is to [specific goal].

Chain of Thought

CopyThink through this step by step:
1. First, analyze [aspect 1]
2. Then, consider [aspect 2]
3. Finally, determine [conclusion]

Show your reasoning before giving the final answer.

Few-Shot Examples

CopyHere are examples of the expected format:

Input: [example 1 input]
Output: [example 1 output]

Input: [example 2 input]
Output: [example 2 output]

Now process this input:
Input: {user_input}
Output:

Structured Output

CopyRespond in the following JSON format:
{
  "analysis": "your analysis here",
  "confidence": 0.0-1.0,
  "recommendations": ["item1", "item2"]
}

Return valid JSON only, no additional text.

Prompt Templates

Code Review

CopyYou are a senior code reviewer. Review the code for:
1. Security vulnerabilities
2. Performance issues
3. Code quality and readability
4. Best practices violations

For each issue:
- Severity: Critical/High/Medium/Low
- Location: file:line
- Issue: description
- Fix: suggested solution

Code to review:
{code}

Data Extraction

CopyExtract the following information from the text:
- Name: person's full name
- Email: email address
- Company: organization name
- Role: job title

If information is not found, use "NOT_FOUND".
Return as JSON.

Text:
{text}

Classification

CopyClassify the following text into one of these categories:
- POSITIVE
- NEGATIVE
- NEUTRAL

Consider tone, sentiment, and overall message.
Respond with only the category name.

Text: {text}
Category:

Best Practices

Testing Prompts

1. Test with edge cases
2. Try adversarial inputs
3. Check consistency across runs
4. Measure output quality
5. Track token usage

Examples

Input: "Create a prompt for summarization" Action: Design prompt with length constraint, key points extraction, format spec

Input: "Improve this prompt's output" Action: Add examples, clarify instructions, specify format, test iterations