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prompt-engineering

Design, test, and version prompts for large language models.

2|Updated Jan 15, 2026
Search Tags:#testing#versioning#prompt engineering#optimization#ai#llm
One-click install
npx skills add https://github.com/DTMC-marketplace/governance --skill prompt-engineering-dtmc-marketplace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/DTMC-marketplace/governance/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/DTMC-marketplace/governance --skill prompt-engineering-dtmc-marketplace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you systematically design, test, and optimize prompts to get the best possible results from AI models, ensuring reliability and efficiency.

Core Features & Use Cases

  • Prompt Design Patterns: Learn and apply various patterns like Zero-shot, Few-shot, and Chain-of-Thought.
  • Testing Framework: Implement a robust testing strategy with defined metrics and test case templates.
  • Versioning: Manage prompt evolution with clear version control and metadata.
  • Use Case: You need to create a prompt for an AI to summarize customer feedback. This Skill provides templates and best practices to ensure the summary is accurate, concise, and captures the key sentiments.

Quick Start

Use the prompt-engineering skill to design a new prompt for customer service interactions.

Frequently Asked Questions about prompt-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to structure prompts for large language models?▼

Prompt engineering uses design patterns like Zero-shot, Few-shot, and Chain-of-Thought to structure LLM prompts. These templates help systematically define AI interactions to achieve reliable and optimized outputs.

How do I test and evaluate LLM prompts for optimization?▼

You can test LLM prompts using a robust framework with defined metrics and test case templates. This systematic testing strategy ensures your prompts generate accurate, reproducible results across various inputs.

Can I apply version control to manage prompt evolution for AI models?▼

Yes, prompt versioning manages AI prompt evolution with clear version control and metadata. This strategy addresses the need for reproducible interactions by tracking changes and maintaining prompt history.

When do I need few-shot versus zero-shot patterns for AI interactions?▼

Use few-shot patterns when the AI needs contextual examples to perform accurately, while zero-shot relies on direct instructions. Prompt engineering applies these structures to match your specific task complexity.

How do I create a prompt for an AI to summarize customer feedback?▼

Use prompt engineering templates and best practices to design an AI prompt for summarizing customer feedback. This ensures the generated summary is accurate, concise, and captures key sentiments effectively.

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