Can I use these patterns to reduce API costs?

Yes, the skill includes token efficiency patterns that help remove redundant instructions and consolidate prompts without sacrificing the quality of the AI's response.

How does this skill help with production LLM apps?

It provides frameworks for testing, versioning, and optimizing prompts to ensure they handle edge cases and maintain high performance at scale.

What are prompt engineering patterns?

They are reusable structures and techniques, such as few-shot learning and Chain-of-Thought, designed to improve the reliability, accuracy, and logic of AI model outputs.

Does it support dynamic context for RAG?

Absolutely. It covers strategies for semantic similarity retrieval to inject the most relevant examples and context into your prompts dynamically for Retrieval-Augmented Generation.

What is Chain-of-Thought prompting?

Chain-of-Thought (CoT) is a technique that encourages the model to generate intermediate reasoning steps before providing a final answer, which significantly improves performance on complex tasks.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: EngineerWithAI

byEngineerWithAI

データサイエンスとML

Optimizes Large Language Model performance through advanced reasoning patterns, few-shot learning, and structured prompt templates.

概要

This skill empowers developers to build production-grade LLM applications by applying sophisticated prompt engineering techniques. It provides standardized patterns for Chain-of-Thought reasoning, dynamic few-shot example selection, and modular template systems to ensure AI outputs are consistent, reliable, and cost-effective. Whether you are debugging inconsistent model responses or designing complex system prompts for specialized agents, this skill offers a comprehensive blueprint for maximizing the reasoning capabilities and controllability of Claude and other advanced AI models.

主な機能

Iterative prompt optimization and performance benchmarking
Modular template systems with variable interpolation
Few-shot learning strategies with dynamic example selection
System prompt design for specialized AI assistant behavior
Chain-of-Thought (CoT) and multi-step reasoning elicitation
0 GitHub stars

ユースケース

Optimizing token usage and reducing latency in high-volume LLM workflows
Improving reasoning capabilities for multi-step logical tasks and data extraction
Designing complex prompts for production-ready AI applications

概要

主な機能

Iterative prompt optimization and performance benchmarking
Modular template systems with variable interpolation
Few-shot learning strategies with dynamic example selection
System prompt design for specialized AI assistant behavior
Chain-of-Thought (CoT) and multi-step reasoning elicitation
0 GitHub stars

ユースケース

Optimizing token usage and reducing latency in high-volume LLM workflows
Improving reasoning capabilities for multi-step logical tasks and data extraction
Designing complex prompts for production-ready AI applications