Does this skill help reduce API costs?

Yes, it includes performance optimization patterns specifically designed to reduce token usage and latency while maintaining high-quality outputs.

What are the main benefits of using this skill?

It enhances LLM output reliability, controllability, and accuracy by providing standardized, battle-tested prompting patterns like CoT and few-shot learning.

Can I use these patterns for RAG applications?

Absolutely. The skill includes specific integration patterns for combining retrieved context with engineering best practices to ensure grounded responses.

What reasoning techniques does it support?

It supports a wide range of reasoning elicitation methods including Zero-shot CoT, Few-shot CoT, and self-consistency techniques for complex problem solving.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

데이터 과학 및 ML

Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.

소개

Prompt Engineering Patterns is a specialized Claude Code skill designed to help developers build production-grade LLM applications with high precision and controllability. It provides a comprehensive framework for implementing complex reasoning traces, dynamic few-shot example selection, and robust template systems. By applying these patterns, users can significantly improve model consistency, reduce token overhead, and handle difficult edge cases through sophisticated system prompt designs and automated error recovery strategies.

주요 기능

Systematic prompt optimization and performance benchmarking
Error recovery and self-verification pattern implementation
Dynamic Few-Shot Learning with semantic similarity selection
Structured Chain-of-Thought reasoning for complex logic
Reusable prompt templates with variable interpolation
0 GitHub stars

사용 사례

Optimizing token usage and latency for high-traffic LLM features
Reducing hallucinations and improving consistency in data extraction
Designing reliable prompts for production-grade AI agents

소개

주요 기능

Systematic prompt optimization and performance benchmarking
Error recovery and self-verification pattern implementation
Dynamic Few-Shot Learning with semantic similarity selection
Structured Chain-of-Thought reasoning for complex logic
Reusable prompt templates with variable interpolation
0 GitHub stars

사용 사례

Optimizing token usage and latency for high-traffic LLM features
Reducing hallucinations and improving consistency in data extraction
Designing reliable prompts for production-grade AI agents