What is RAG and why should I use this skill?

RAG (Retrieval-Augmented Generation) connects LLMs to external data sources. Use this skill to build systems that provide accurate, up-to-date responses grounded in your specific documents.

Which vector databases does this skill support?

It provides implementation patterns for major providers including Pinecone, Weaviate, Chroma, Milvus, Qdrant, and FAISS.

Does it include document chunking strategies?

Yes, it covers various methods including Recursive Character, Token-Based, Semantic, and Markdown Header splitting to optimize how your data is indexed.

How does this skill help reduce AI hallucinations?

By providing prompts for grounding, source citations, and confidence scoring, alongside high-quality retrieval, it ensures the LLM stays within the provided context.

RAG Implementation Expert

Name: RAG Implementation Expert
Author: amurata

byamurata

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데이터 과학 및 ML

Builds sophisticated Retrieval-Augmented Generation (RAG) systems to ground LLM responses with external knowledge and proprietary data.

소개

This skill provides comprehensive guidance and implementation patterns for building Retrieval-Augmented Generation (RAG) systems within LLM applications. It masters the integration of vector databases, embedding models, and advanced retrieval strategies like hybrid search and reranking to significantly reduce hallucinations and provide domain-specific knowledge. Whether you are building a documentation assistant, a research tool with source citations, or a Q&A system over private documents, this skill offers the architectures and code snippets needed to ensure factual, grounded, and high-performance AI responses.

주요 기능

Comprehensive support for vector databases including Pinecone, Chroma, and Weaviate
3 GitHub stars
Diverse document chunking strategies for optimal context window management
Integration of reranking techniques using Cross-Encoders and Cohere for higher precision
Built-in evaluation metrics for measuring accuracy, retrieval quality, and groundedness
Advanced retrieval patterns such as hybrid search, multi-query, and contextual compression

사용 사례

Creating AI-powered research assistants that provide verifiable source citations
Building factual chatbots that access real-time information to prevent hallucinations
Developing internal Q&A systems over proprietary company documentation

소개

주요 기능

Comprehensive support for vector databases including Pinecone, Chroma, and Weaviate
3 GitHub stars
Diverse document chunking strategies for optimal context window management
Integration of reranking techniques using Cross-Encoders and Cohere for higher precision
Built-in evaluation metrics for measuring accuracy, retrieval quality, and groundedness
Advanced retrieval patterns such as hybrid search, multi-query, and contextual compression

사용 사례

Creating AI-powered research assistants that provide verifiable source citations
Building factual chatbots that access real-time information to prevent hallucinations
Developing internal Q&A systems over proprietary company documentation