prompt-engineering-patterns

Design and optimize prompts for production LLM applications.

15|10|Updated May 21, 2026

Search Tags:#prompt engineering #few-shot learning #LLM #performance optimization #structured outputs #template systems

Ready to run

npx skills add https://github.com/The-AIOS/aios --skill prompt-engineering-patterns-the-aios

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/The-AIOS/aios/tree/main/skills/aios/prompt-engineering-patterns
Command: npx skills add https://github.com/The-AIOS/aios --skill prompt-engineering-patterns-the-aios

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production settings.

Core Features & Use Cases

Complex Prompt Design: Develop prompts for production LLM applications.
Optimization: Enhance prompt performance and consistency.
Reasoning Patterns: Implement structured reasoning for better LLM output.
Dynamic Examples: Create few-shot learning systems with dynamic example selection.
Prompt Templates: Build reusable prompt templates with variable interpolation.
Debugging: Refine prompts that produce inconsistent outputs.
System Prompt Design: Create system prompts for specialized AI assistants.
Structured Outputs: Use JSON mode for reliable parsing.
Template Systems: Develop variable interpolation and conditional prompt sections.

Quick Start

Use the prompt-engineering-patterns skill to apply advanced prompt engineering techniques to your LLM applications.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema

How do I improve LLM output consistency in production applications?▼

Fix inconsistent LLM outputs by debugging and refining prompts using structured reasoning patterns, dynamic few-shot example selection, and JSON mode for reliable parsing. These prompt engineering techniques enforce controllability and consistency in production applications.

What's the best way to structure few-shot learning examples for LLMs?▼

Structure few-shot learning by creating dynamic example selection systems within prompt templates. This approach interpolates relevant examples into the prompt, guiding the LLM to produce more accurate and targeted outputs for your specific use case.

How do I create reusable prompt templates with variable interpolation?▼

Create reusable prompt templates by building template systems with variable interpolation and conditional prompt sections. This allows you to dynamically generate structured prompts for production LLM applications while maintaining structural consistency.

Why does my LLM output fail when parsing structured JSON data?▼

LLM output parsing fails when prompts lack structured output constraints. Use JSON mode and structured reasoning patterns in your prompt engineering to ensure the LLM generates valid, parseable JSON data for your application pipeline.

Do I need advanced prompt engineering concepts to design system prompts?▼

Yes, designing system prompts for specialized AI assistants requires understanding advanced prompt engineering concepts. You must implement structured reasoning and template systems to maximize LLM performance, reliability, and controllability.