How does this skill help reduce token usage?

It provides optimization techniques for instruction hierarchy and content consolidation, helping you remove redundant phrases while maintaining model performance.

Can I use these patterns for production AI applications?

Yes, these patterns are specifically designed to move prompts from experimental stages to reliable, production-grade outputs with measurable performance metrics.

Does it support dynamic example selection?

Yes, the skill covers strategies for semantic similarity and diversity sampling to select the most relevant few-shot examples from a knowledge base dynamically.

What are prompt engineering patterns?

Prompt engineering patterns are standardized strategies like few-shot learning, chain-of-thought, and progressive disclosure used to improve AI accuracy, reliability, and consistency.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: amurata

byamurata

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データサイエンスとML

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

概要

This skill provides a comprehensive framework for building production-grade LLM applications by implementing proven prompt engineering strategies. It enables developers to maximize model controllability and output quality through structured reasoning, dynamic example selection, and iterative refinement workflows. By standardizing the way prompts are constructed—from system context to output formatting—it helps eliminate inconsistent results and reduces token consumption in complex AI workflows.

主な機能

System prompt design for specialized model behavior
Advanced few-shot learning and dynamic example selection
Modular prompt templates with variable interpolation
3 GitHub stars
Iterative optimization workflows and performance metrics
Chain-of-thought and structured reasoning elicitation

ユースケース

Reducing hallucinations in complex logic or coding workflows
Building reusable prompt libraries for enterprise AI applications
Optimizing production LLM prompts for high-consistency tasks

概要

主な機能

System prompt design for specialized model behavior
Advanced few-shot learning and dynamic example selection
Modular prompt templates with variable interpolation
3 GitHub stars
Iterative optimization workflows and performance metrics
Chain-of-thought and structured reasoning elicitation

ユースケース

Reducing hallucinations in complex logic or coding workflows
Building reusable prompt libraries for enterprise AI applications
Optimizing production LLM prompts for high-consistency tasks