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prompt-engineering-patterns

Optimize vague user requests into structured prompts for LLM execution.

1|Updated Feb 1, 2026
Search Tags:#prompt engineering#chain-of-thought#few-shot learning#structured output#llm optimization#request optimization
One-click install
npx skills add https://github.com/eroslifestyle/Claude-Orchestrator-Plugin --skill prompt-engineering-patterns-eroslifestyle
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/eroslifestyle/Claude-Orchestrator-Plugin/tree/main/prompt-engineering-patterns
Command: npx skills add https://github.com/eroslifestyle/Claude-Orchestrator-Plugin --skill prompt-engineering-patterns-eroslifestyle

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of crafting effective prompts for Large Language Models (LLMs), ensuring clarity, reliability, and control over AI outputs.

Core Features & Use Cases

  • Prompt Optimization: Refine vague or complex user requests into structured, actionable prompts.
  • Structured Output Generation: Learn to use patterns like Chain-of-Thought and Few-Shot Learning for better LLM performance.
  • Use Case: When a user asks to "fix the code," this skill can transform it into a detailed, structured request specifying the bug, context, and desired outcome, guiding the LLM to provide a precise solution.

Quick Start

Use the prompt-engineering-patterns skill to optimize the request "make the website better".

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I optimize LLM prompts for better performance?▼

To optimize LLM prompts, you refine vague requests into structured, actionable inputs by applying techniques like Context-First and structured output formats, maximizing model reliability and control.

What is chain-of-thought prompt engineering?▼

Chain-of-thought is an advanced prompt engineering pattern that structures LLM reasoning steps. It guides the model to process context sequentially, improving output accuracy for complex problem-solving tasks.

How can I use few-shot learning to improve LLM output reliability?▼

Few-shot learning improves LLM reliability by providing specific examples within the prompt. This pattern trains the model contextually, ensuring structured output generation that matches your desired format and tone.

What's the best way to expand vague user requests for LLMs?▼

The best way to expand vague LLM requests is pre-processing them into detailed, structured prompts. Specify the exact context, desired outcome, and constraints to guide the model toward a precise solution.

When do I need structured output formats in prompt engineering?▼

You need structured output formats in prompt engineering when executing orchestrator tasks or requiring consistent, parseable responses. This approach ensures controllability and reliability across complex LLM workflows.

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