Can this help with RAG implementation?

Absolutely, it offers detailed best practices for chunking strategies, retrieval quality factors, and hybrid search methods to improve the relevance of retrieved data.

Does it help with cost management?

Yes, it includes specific strategies for token efficiency and instruction compression to reduce LLM operational costs while maintaining output quality.

What is the primary benefit of this skill?

It provides expert-level guidance on structuring prompts and AI architectures to improve the reliability and reasoning capabilities of Claude in your development projects.

How does it improve agent workflows?

It introduces patterns like ReAct (Thought-Action-Observation) and specific tool design principles to create more stable and effective autonomous AI agents.

Prompt Engineering & RAG Guide

Name: Prompt Engineering & RAG Guide
Author: eyadsibai

byeyadsibai

数据科学与机器学习

Optimizes LLM performance through advanced prompt engineering, RAG system design, and agent workflow orchestration.

关于

The Prompt Engineering skill provides a comprehensive framework for building high-performing AI applications within Claude Code. It offers structured guidance on implementing Chain of Thought reasoning, multi-shot learning, and sophisticated RAG (Retrieval-Augmented Generation) architectures. Whether you are designing autonomous agent workflows using the ReAct pattern or optimizing token efficiency, this skill serves as a domain-specific expert to ensure your LLM integrations are reliable, accurate, and cost-effective.

主要功能

Chain of Thought (CoT) reasoning strategies
Token efficiency and output quality improvement techniques
0 GitHub stars
Agent workflow design using the ReAct pattern
RAG architecture and chunking strategy optimization
Few-shot learning and pattern demonstration templates

使用场景

Designing high-accuracy RAG systems for private documentation
Refining complex prompts for structured data extraction
Building autonomous agents with tool-calling capabilities

关于

主要功能

Chain of Thought (CoT) reasoning strategies
Token efficiency and output quality improvement techniques
0 GitHub stars
Agent workflow design using the ReAct pattern
RAG architecture and chunking strategy optimization
Few-shot learning and pattern demonstration templates

使用场景

Designing high-accuracy RAG systems for private documentation
Refining complex prompts for structured data extraction
Building autonomous agents with tool-calling capabilities