What is a RAG system?

Retrieval Augmented Generation (RAG) is a framework that retrieves relevant documents from an external knowledge base and provides them to an LLM as context to generate more accurate, grounded responses.

How do I evaluate the quality of my RAG pipeline?

The skill provides templates for measuring faithfulness (is the answer grounded in context?), relevance (is the retrieved context useful?), and answer quality (how does it compare to expected output?).

Does this skill support hybrid search?

Yes, it includes implementation patterns for combining dense vector retrieval with sparse keyword matching (BM25) to get the benefits of both semantic understanding and exact term matching.

Which embedding models can I use with this skill?

The skill demonstrates patterns for both SentenceTransformers (local) and OpenAIEmbeddings, but the architecture is designed to be model-agnostic.

RAG Systems Builder

Name: RAG Systems Builder
Author: pluginagentmarketplace

bypluginagentmarketplace

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Ciencia de Datos y ML

Builds production-grade Retrieval Augmented Generation pipelines with vector search, hybrid retrieval, and advanced re-ranking strategies.

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The RAG Systems skill empowers developers to architect and implement sophisticated Retrieval Augmented Generation pipelines for AI applications. It provides comprehensive guidance on document processing, intelligent chunking strategies, and hybrid retrieval methods that combine dense semantic search with sparse keyword matching. Beyond basic setups, it includes production-ready patterns such as cross-encoder re-ranking, query expansion, and automated evaluation metrics like faithfulness and relevance, making it an essential resource for building context-aware AI agents that utilize private datasets.

Características Principales

Advanced document processing and recursive chunking strategies
Automated RAG evaluation metrics for faithfulness and relevance
Cross-encoder re-ranking for superior answer precision
Query expansion techniques to improve retrieval recall
2 GitHub stars
Hybrid retrieval patterns combining semantic and BM25 search

Casos de Uso

Building a custom AI-powered assistant for internal documentation
Reducing LLM hallucinations through grounded context retrieval
Implementing semantic search for complex corporate knowledge bases

Acerca de

Características Principales

Advanced document processing and recursive chunking strategies
Automated RAG evaluation metrics for faithfulness and relevance
Cross-encoder re-ranking for superior answer precision
Query expansion techniques to improve retrieval recall
2 GitHub stars
Hybrid retrieval patterns combining semantic and BM25 search

Casos de Uso

Building a custom AI-powered assistant for internal documentation
Reducing LLM hallucinations through grounded context retrieval
Implementing semantic search for complex corporate knowledge bases