Can I use these patterns for production applications?

Yes, the skill is specifically designed for production environments, focusing on error recovery, token efficiency, and version-controlled prompt architectures.

How do I reduce costs using these prompt patterns?

The skill includes performance optimization techniques such as removing redundant instructions, consolidating stable content into system prompts, and optimizing context windows.

Does it support dynamic example selection?

Yes, it provides logic for few-shot selectors that use semantic similarity to choose the most relevant examples for a given user query from a database.

How does this skill improve LLM reliability?

It implements structured reasoning patterns like Chain-of-Thought and strategic few-shot examples that guide the model toward consistent, accurate, and verifiable outputs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

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

About

Prompt Engineering Patterns is a specialized skill for Claude Code designed to help developers build production-grade AI applications with higher accuracy and consistency. It provides a structured framework for implementing sophisticated techniques such as multi-step chain-of-thought reasoning, dynamic few-shot learning with semantic example selection, and modular prompt template systems. By applying these battle-tested patterns, developers can reduce hallucination, improve model controllability, and streamline the iterative process of prompt optimization for specialized AI assistants and complex data workflows.

Key Features

Systematic prompt optimization and performance benchmarking
Dynamic few-shot learning with semantic similarity selection
0 GitHub stars
Implementation of multi-step Chain-of-Thought reasoning
Modular prompt template systems with variable interpolation
Advanced system prompt design for specialized model behaviors

Use Cases

Optimizing SQL generation or code translation accuracy through structured examples
Designing high-reliability prompts for production AI agents and assistants
Reducing token costs while maintaining output quality in high-volume applications

About

Key Features

Systematic prompt optimization and performance benchmarking
Dynamic few-shot learning with semantic similarity selection
0 GitHub stars
Implementation of multi-step Chain-of-Thought reasoning
Modular prompt template systems with variable interpolation
Advanced system prompt design for specialized model behaviors

Use Cases

Optimizing SQL generation or code translation accuracy through structured examples
Designing high-reliability prompts for production AI agents and assistants
Reducing token costs while maintaining output quality in high-volume applications