Is this skill suitable for production environments?

Absolutely. It focuses on reliability and controllability, offering patterns for error recovery, validation steps, and performance metrics tracking specifically for production AI applications.

What is the Prompt Engineering Patterns skill?

It is a specialized toolset for Claude Code designed to help developers implement advanced techniques that improve the accuracy, consistency, and efficiency of Large Language Model outputs.

Can this skill help reduce my API costs?

Yes, it includes performance optimization patterns focused on token efficiency, helping you remove redundant instructions and consolidate prompts without sacrificing quality.

How does few-shot learning work in this skill?

The skill provides patterns for selecting and presenting high-quality examples to the AI, using strategies like semantic similarity to ensure the model has the most relevant context for a given task.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: Cursedpotential

byCursedpotential

Ciencia de Datos y ML

Optimizes LLM performance and reliability through advanced techniques like few-shot learning, chain-of-thought reasoning, and structured template design.

Acerca de

The Prompt Engineering Patterns skill provides a comprehensive framework for mastering LLM interactions, enabling developers to build production-grade AI applications with high precision. It implements sophisticated patterns such as dynamic few-shot example selection, multi-step reasoning elicitation, and modular prompt templates to solve the common challenges of output inconsistency and token inefficiency. Whether you are building complex RAG systems or specialized AI assistants, this skill offers the tools and best practices needed to ensure your prompts are robust, controllable, and performant.

Características Principales

Automated prompt optimization and iterative refinement workflows
System-level design for specialized AI roles and constraints
Chain-of-thought (CoT) and self-consistency reasoning elicitation
Dynamic few-shot learning with semantic similarity selection
Structured prompt template systems with variable interpolation
0 GitHub stars

Casos de Uso

Designing production-ready prompts for high-reliability AI applications
Implementing structured reasoning for logic-intensive coding or analysis tasks
Reducing token consumption and latency in complex LLM workflows

Acerca de

Características Principales

Automated prompt optimization and iterative refinement workflows
System-level design for specialized AI roles and constraints
Chain-of-thought (CoT) and self-consistency reasoning elicitation
Dynamic few-shot learning with semantic similarity selection
Structured prompt template systems with variable interpolation
0 GitHub stars

Casos de Uso

Designing production-ready prompts for high-reliability AI applications
Implementing structured reasoning for logic-intensive coding or analysis tasks
Reducing token consumption and latency in complex LLM workflows