prompt-engineering-patterns

Design robust prompts for reliable AI outputs using structured patterns.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/Hanseooo/hanseo-opencode-workflows --skill prompt-engineering-patterns-hanseooo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/Hanseooo/hanseo-opencode-workflows/tree/main/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/Hanseooo/hanseo-opencode-workflows --skill prompt-engineering-patterns-hanseooo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Design robust prompts for reliable AI outputs.

Core Features & Use Cases

  • Pattern-based techniques for few-shot, chain-of-thought, structured outputs, and system prompts
  • Templates and best practices for evaluation, error handling, and risk mitigation
  • Production-ready workflows: prompt templates, dynamic example selection, and verification patterns

Quick Start

Provide a real-world task and, using one of the documented patterns, generate a robust prompt template to guide the model.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
What are prompt engineering patterns for reliable LLM outputs?▼

Prompt engineering patterns are structured techniques like few-shot, chain-of-thought, and system prompts that enforce practical requirements such as structured outputs and hallucination prevention for reliable AI generation.

How do I design robust prompts to prevent AI hallucinations in production?▼

To prevent AI hallucinations, design robust prompts using pattern-driven workflows that incorporate verification patterns, error handling best practices, and structured outputs to enforce safe, repeatable LLM behaviors.

When should I use chain-of-thought vs few-shot prompting techniques?▼

Use chain-of-thought prompting for complex reasoning tasks and few-shot prompting to provide contextual examples, applying these distinct pattern-based techniques to generate reliable outputs for specific production AI tasks.

What's the best way to generate structured outputs from an LLM?▼

The best way to generate structured outputs is applying specific prompt engineering patterns that enforce formatting requirements, utilizing prompt templates and dynamic example selection within production-ready workflows.

Can I use these prompt templates for evaluating and refining existing prompts?▼

Yes, you can use these prompt templates for evaluating and refining existing prompts, as the pattern-driven approach includes best practices for evaluation, error handling, and risk mitigation across production AI tasks.