What makes this RAG skill different from standard vector search?

This skill moves beyond simple 'chunk and embed' approaches by incorporating hybrid search, semantic chunking, and contextual reranking to ensure significantly higher retrieval accuracy.

Which vector databases and embedding models does it support?

The skill provides architectural patterns and best practices that are agnostic to specific providers, focusing on universal principles for vector stores and embedding consistency.

Does this skill help with RAG performance issues?

Yes, it identifies 'sharp edges' like high latency and provides specific optimization strategies to keep your retrieval pipeline responsive for production use.

Why is chunking strategy so important for RAG?

Poor chunking leads to loss of context; this skill helps implement recursive character splitting and semantic boundaries to maintain document meaning and improve LLM comprehension.

RAG Implementation Expert

Name: RAG Implementation Expert
Author: claudiodearaujo

byclaudiodearaujo

데이터 과학 및 ML

Implements high-performance Retrieval-Augmented Generation systems using advanced chunking, hybrid search, and reranking strategies.

소개

This skill transforms Claude into a RAG specialist capable of architecting sophisticated document retrieval systems that go beyond simple vector search. It provides expert guidance on critical components such as semantic chunking, embedding model consistency, and hybrid search techniques that combine dense and sparse retrieval. By identifying common pitfalls like fixed-size chunking and high-latency pipelines, it ensures that your AI applications deliver highly relevant, context-aware information from massive datasets while maintaining performance and accuracy.

주요 기능

Hybrid search optimization combining vector and keyword retrieval
Latency optimization for production-grade RAG pipelines
Advanced semantic and recursive document chunking patterns
Embedding model management and consistency enforcement
0 GitHub stars
Contextual reranking strategies to improve document relevance

사용 사례

Building enterprise-scale document search and Q&A systems
Reducing LLM hallucinations by providing precise context windows
Migrating from naive vector search to sophisticated hybrid retrieval

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front rag-implementation

For use in Claude.ai and ChatGPT

Download Skill

GitHub

소개