Does this skill help with token optimization?

Yes, it includes specific best practices and patterns for reducing token usage and minimizing latency without sacrificing the accuracy of the model.

How does it handle inconsistent model outputs?

It offers strategies for iterative refinement, error recovery patterns, and structured instruction hierarchies to ensure more predictable and consistent model behavior across different inputs.

What is included in the instruction hierarchy pattern?

The pattern provides a logical structure for prompts following a sequence: System Context, followed by Task Instruction, Examples, Input Data, and finally the Output Format specification.

What is the Prompt Engineering Patterns skill?

It is a specialized capability for Claude Code that helps developers implement advanced techniques like few-shot learning and Chain-of-Thought to improve LLM reliability and output quality.

Can I use this for production RAG systems?

Absolutely. The skill provides specific integration patterns for combining retrieved context with optimized prompt structures to enhance RAG (Retrieval-Augmented Generation) applications.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: Slowth-KIM

bySlowth-KIM

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Ciencia de Datos y ML

Optimizes LLM performance and reliability through advanced prompt engineering techniques and structured reasoning patterns.

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Prompt Engineering Patterns is a specialized skill for Claude Code designed to maximize the performance, controllability, and reliability of large language models in production environments. It provides developers with a robust framework for implementing complex patterns like Chain-of-Thought reasoning, few-shot learning, and dynamic template systems. Whether you are refining system prompts for specialized assistants or debugging inconsistent model outputs, this skill offers the implementation patterns and best practices needed to turn raw LLM interactions into predictable, high-quality production features.

Características Principales

Prompt optimization workflows to reduce latency and token usage.
Implementation of Chain-of-Thought (CoT) and structured reasoning patterns.
1 GitHub stars
Modular prompt template systems with variable interpolation and formatting.
Advanced Few-Shot Learning strategies with dynamic example selection.
System prompt design for specialized model behaviors and safety constraints.

Casos de Uso

Standardizing reasoning paths through Chain-of-Thought techniques.
Building dynamic few-shot systems for specialized domain tasks like SQL generation.
Designing high-precision prompts for complex production LLM applications.

Acerca de

Características Principales

Prompt optimization workflows to reduce latency and token usage.
Implementation of Chain-of-Thought (CoT) and structured reasoning patterns.
1 GitHub stars
Modular prompt template systems with variable interpolation and formatting.
Advanced Few-Shot Learning strategies with dynamic example selection.
System prompt design for specialized model behaviors and safety constraints.

Casos de Uso

Standardizing reasoning paths through Chain-of-Thought techniques.
Building dynamic few-shot systems for specialized domain tasks like SQL generation.
Designing high-precision prompts for complex production LLM applications.