What core techniques are included in this skill?

The skill covers Chain-of-Thought (CoT) reasoning, dynamic few-shot learning, systematic prompt optimization, and modular template design.

How does it handle inconsistent LLM outputs?

It provides strategies for self-verification, structured output formatting, and progressive disclosure to ensure reliability across different inputs.

Can this skill help reduce LLM operational costs?

Yes, it includes performance optimization patterns focused on token efficiency and removing redundant instructions without sacrificing output quality.

Does it support dynamic example selection for prompts?

Yes, it includes patterns for few-shot selector strategies like semantic similarity and diversity sampling to provide the most relevant context to the model.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: as4584

byas4584

データサイエンスとML

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production.

概要

This skill provides a comprehensive framework for designing, optimizing, and managing high-performance prompts for LLM applications. It enables developers to implement sophisticated reasoning patterns like Chain-of-Thought, manage dynamic few-shot learning systems, and create structured prompt templates that ensure consistent output. By offering guidance on system prompt design, error recovery strategies, and performance metrics, it helps bridge the gap between experimental prototyping and reliable, production-grade AI services.

主な機能

Modular prompt template systems with variable interpolation and logic
Structured reasoning patterns including Chain-of-Thought and Self-Consistency
0 GitHub stars
Robust error-handling and fallback patterns within AI instructions
Dynamic few-shot learning with semantic example selection and retrieval
Systematic optimization workflows for latency and token efficiency

ユースケース

Building specialized AI assistants with strict behavioral and formatting constraints
Optimizing prompt consistency and reducing token costs for high-volume LLM services
Designing complex reasoning workflows for production AI applications

概要

主な機能

Modular prompt template systems with variable interpolation and logic
Structured reasoning patterns including Chain-of-Thought and Self-Consistency
0 GitHub stars
Robust error-handling and fallback patterns within AI instructions
Dynamic few-shot learning with semantic example selection and retrieval
Systematic optimization workflows for latency and token efficiency

ユースケース

Building specialized AI assistants with strict behavioral and formatting constraints
Optimizing prompt consistency and reducing token costs for high-volume LLM services
Designing complex reasoning workflows for production AI applications