Is this suitable for production AI applications?

Yes, the skill focuses on production-grade reliability, covering error recovery, performance metrics, and version control for prompt management.

Does this skill help with reducing API costs?

Yes, it includes optimization patterns specifically designed to reduce token usage and improve efficiency without sacrificing the quality of the AI's response.

Can I use this for complex reasoning tasks?

Absolutely. The skill includes dedicated patterns for Chain-of-Thought and reasoning elicitation to help the model handle sophisticated logic and multi-step instructions.

What is the Prompt Engineering Patterns skill?

It is a specialized capability for Claude Code that provides frameworks and best practices for creating high-performance, reliable, and controllable prompts for LLM applications.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

데이터 과학 및 ML

Implements advanced prompt engineering techniques to optimize LLM performance, reliability, and controllability in production environments.

소개

The Prompt Engineering Patterns skill provides a comprehensive framework for designing and refining complex LLM interactions. It empowers developers to implement sophisticated strategies such as few-shot learning with dynamic example selection, structured Chain-of-Thought reasoning, and modular template systems with variable interpolation. By following these established patterns, users can maximize the quality of AI outputs, ensure consistent model behavior, and optimize token efficiency, making it an essential tool for building robust, production-grade AI applications and specialized assistants.

주요 기능

Systematic prompt optimization and A/B testing workflows
0 GitHub stars
Modular prompt templates with variable interpolation
Error recovery patterns for consistent and safe model outputs
Dynamic few-shot learning with semantic similarity selection
Chain-of-Thought reasoning patterns for complex logic tasks

사용 사례

Optimizing LLM token usage and latency for high-traffic applications
Building robust data extraction pipelines using structured reasoning
Developing specialized AI agents with strict behavioral constraints

소개

주요 기능

Systematic prompt optimization and A/B testing workflows
0 GitHub stars
Modular prompt templates with variable interpolation
Error recovery patterns for consistent and safe model outputs
Dynamic few-shot learning with semantic similarity selection
Chain-of-Thought reasoning patterns for complex logic tasks

사용 사례

Optimizing LLM token usage and latency for high-traffic applications
Building robust data extraction pipelines using structured reasoning
Developing specialized AI agents with strict behavioral constraints