Does it support hybrid search?

Yes, it includes advanced patterns for combining semantic vector search with keyword-based (BM25) search to capture both conceptual meaning and specific terminology.

What is RAG and why is it used?

Retrieval-Augmented Generation (RAG) is a technique that provides an LLM with specific, external data to ensure its answers are accurate, up-to-date, and grounded in your own documentation rather than just its training data.

How does this skill help with AI hallucinations?

By implementing strict retrieval patterns and grounding prompts, this skill ensures the AI only answers based on the provided context and can include citations to source material.

Can I use this for production applications?

Absolutely. The skill includes best practices for reranking, metadata filtering, and evaluation metrics specifically designed for production-grade reliability.

Which vector databases can I use with this skill?

This skill provides implementation patterns for several popular options including Pinecone (managed), Weaviate (hybrid), Chroma (local/lightweight), and FAISS (local library).

RAG Implementation

Name: RAG Implementation
Author: Microck

byMicrock

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数据科学与机器学习

Implements Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground LLM applications in external knowledge.

This skill provides a comprehensive framework for building production-ready RAG systems, enabling Claude to architect applications that access and reason over proprietary or domain-specific datasets. It guides users through the entire RAG lifecycle, including document ingestion, sophisticated chunking strategies, embedding generation, and advanced retrieval patterns such as hybrid search and semantic reranking. By leveraging these patterns, developers can significantly reduce hallucinations and build high-accuracy Q&A systems, documentation assistants, and research tools that provide verifiable source citations.

主要功能

01Advanced document chunking and recursive splitting strategies

02Comprehensive evaluation metrics for groundedness and retrieval quality

03Hybrid search patterns combining semantic and keyword retrieval

04Multi-vector store integration including Pinecone, Chroma, and Weaviate

0581 GitHub stars

06Contextual compression and reranking for high-precision results

使用场景

01Reducing AI hallucinations by grounding responses in verified knowledge bases

02Building enterprise-grade Q&A systems over private document repositories

03Creating documentation assistants with automated source citations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills rag-implementation

For use in Claude.ai and ChatGPT

Download Skill