What is RAG in the context of LLM development?

Retrieval-Augmented Generation (RAG) is a technique that provides LLMs with access to specific, external data at query time to ensure responses are accurate, current, and grounded in fact.

Which vector databases can I use with this skill?

The skill provides implementation patterns for a wide range of databases including Pinecone, Weaviate, Chroma, Milvus, Qdrant, and FAISS.

How does this skill help reduce AI hallucinations?

It implements groundedness prompts and retrieval-centric workflows that force the model to answer based only on the provided context and include source citations.

Does it support advanced retrieval techniques?

Yes, it includes advanced patterns like hybrid search (combining keyword and semantic search), multi-query expansion, and reranking with cross-encoders for higher precision.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Implements robust Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.

About

This skill provides a comprehensive framework for building knowledge-grounded AI applications by integrating Large Language Models with external data sources. It guides developers through the entire RAG pipeline—from document chunking and embedding generation to vector storage and advanced retrieval strategies like hybrid search and reranking. By leveraging these patterns, users can effectively reduce hallucinations, provide source citations, and enable AI models to access proprietary or domain-specific knowledge with high accuracy and efficiency.

Key Features

Comprehensive evaluation framework for tracking accuracy, retrieval quality, and groundedness
Sophisticated retrieval patterns including Hybrid Search, Multi-Query, and Parent Document Retrieval
Advanced document chunking strategies including recursive, semantic, and markdown splitting
Support for multiple vector databases like Pinecone, Chroma, Weaviate, and Qdrant
0 GitHub stars
Post-retrieval optimization through cross-encoder reranking and contextual compression

Use Cases

Implementing research tools that provide accurate answers with verifiable source citations
Building Q&A systems over proprietary internal documentation and private knowledge bases
Creating chatbots that require real-time access to factual, external information

About

Key Features

Comprehensive evaluation framework for tracking accuracy, retrieval quality, and groundedness
Sophisticated retrieval patterns including Hybrid Search, Multi-Query, and Parent Document Retrieval
Advanced document chunking strategies including recursive, semantic, and markdown splitting
Support for multiple vector databases like Pinecone, Chroma, Weaviate, and Qdrant
0 GitHub stars
Post-retrieval optimization through cross-encoder reranking and contextual compression

Use Cases

Implementing research tools that provide accurate answers with verifiable source citations
Building Q&A systems over proprietary internal documentation and private knowledge bases
Creating chatbots that require real-time access to factual, external information