What is RAG in the context of this skill?

RAG (Retrieval-Augmented Generation) is a technique that provides LLMs with specific, external data to generate more accurate, context-aware responses and minimize hallucinations.

Is LangChain code included in the implementation guide?

Yes, the skill features production-ready Python snippets using LangChain for document loading, splitting, and building retrieval chains.

Which vector databases are supported?

The skill provides patterns and configurations for Pinecone, Weaviate, Milvus, Chroma, Qdrant, and FAISS.

How do I evaluate the quality of my RAG system?

This skill includes evaluation metrics for accuracy, retrieval quality, and groundedness, helping you systematically measure how well your system performs.

Does this skill include advanced retrieval strategies?

Yes, it covers multi-query retrieval, hybrid search (combining dense and sparse results), and contextual compression to optimize context window usage.

RAG Implementation

Name: RAG Implementation
Author: camoneart

bycamoneart

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Data Science & ML

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

This skill provides comprehensive guidance and implementation patterns for building advanced RAG pipelines. It covers everything from initial document chunking and embedding generation to sophisticated retrieval strategies like hybrid search and reranking. Ideal for developers building documentation assistants, proprietary knowledge bases, or research tools, it helps ensure AI responses are accurate, grounded, and free from hallucinations by leveraging industry-standard vector databases like Pinecone, Chroma, and Weaviate alongside advanced LangChain patterns.

Key Features

01Advanced document chunking strategies including recursive and semantic splitting

02Reranking techniques using Cross-Encoders and contextual compression

033 GitHub stars

04Implementation patterns for semantic, sparse, and hybrid search retrieval

05Vector database configuration for Pinecone, Weaviate, Chroma, and FAISS

06Standardized evaluation metrics for RAG accuracy, retrieval quality, and groundedness

Use Cases

01Creating research tools that provide verified source citations for every AI claim

02Reducing hallucinations in chatbots by forcing grounding in factual knowledge bases

03Building a Q&A system over internal company documentation or proprietary datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add camoneart/claude-code rag-implementation

For use in Claude.ai and ChatGPT

Download Skill