How can I measure the performance of my RAG system?

The skill provides evaluation frameworks to measure key metrics such as answer accuracy, retrieval quality, and the groundedness of the generated responses.

How does this skill handle document chunking?

It provides multiple strategies including Recursive Character splitting, Token-based splitting, and Semantic Chunking to ensure context is preserved during retrieval.

What is RAG and why should I use it?

RAG (Retrieval-Augmented Generation) allows LLMs to access specific external knowledge, reducing hallucinations and providing contextually accurate, grounded responses based on your own documents.

Does it support hybrid search capabilities?

Yes, it includes advanced patterns for combining dense semantic retrieval with sparse keyword search (BM25) to improve the relevance of retrieved documents.

Which vector databases are supported by this skill?

This skill includes implementation patterns and configuration guides for Pinecone, Weaviate, Chroma, Qdrant, Milvus, and FAISS.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: HermeticOrmus

byHermeticOrmus

0•

Data Science & ML

Builds sophisticated Retrieval-Augmented Generation systems to ground LLM applications with proprietary data and external knowledge.

This skill provides comprehensive patterns and best practices for implementing Retrieval-Augmented Generation (RAG) in AI applications. It guides developers through the entire pipeline, from document chunking and embedding generation to vector database configuration and advanced retrieval strategies like hybrid search and reranking. By following these implementation patterns, developers can build Q&A systems and documentation assistants that provide accurate, source-cited responses while significantly reducing model hallucinations and ensuring groundedness.

Key Features

010 GitHub stars

02Comprehensive vector database configurations for Pinecone, Chroma, and Weaviate

03Advanced retrieval patterns such as Hybrid Search, Multi-Query, and Reranking

04Optimized document chunking strategies including semantic and recursive splitting

05Integration workflows for popular embedding models and reranking libraries

06Built-in evaluation metrics for groundedness, accuracy, and retrieval quality

Use Cases

01Developing research tools that require natural language search and source citations

02Creating domain-specific chatbots grounded in frequently updated knowledge bases

03Building a Q&A system for proprietary technical documentation or internal wikis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/hermetic-line rag-implementation

For use in Claude.ai and ChatGPT

Download Skill