What is RAG in LLM application development?

Retrieval-Augmented Generation (RAG) is a technique that grants LLMs access to external, specific data to provide more accurate, up-to-date, and context-aware responses.

Does this skill include advanced retrieval techniques?

Yes, it covers hybrid search (combining keyword and semantic search), multi-query retrieval, and reranking with cross-encoders for higher precision.

Which vector databases does this skill support?

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

How does RAG help reduce LLM hallucinations?

By forcing the LLM to use retrieved context from verified documents to answer questions, it grounds the model's response in facts rather than relying solely on its internal training data.

Is this RAG implementation compatible with LangChain?

Yes, the provided code snippets and architectural patterns are primarily built using the LangChain framework for Python.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: HermeticOrmus

byHermeticOrmus

データサイエンスとML

Implements robust Retrieval-Augmented Generation systems to connect LLMs with external knowledge bases and vector databases.

概要

This skill provides a comprehensive framework for building Retrieval-Augmented Generation (RAG) systems, enabling LLM applications to provide accurate, grounded responses using proprietary or domain-specific data. It covers the entire pipeline, from document chunking and embedding generation to advanced retrieval strategies like hybrid search and reranking. Whether you are building a documentation assistant, a research tool, or a factual Q&A chatbot, this skill offers production-ready patterns and configurations for popular vector databases like Pinecone, Chroma, and Weaviate to help reduce hallucinations and improve AI reliability.

主な機能

Integration with leading vector databases including Pinecone, Chroma, and Weaviate
Advanced retrieval patterns including hybrid search, multi-query, and contextual compression
Prompt engineering templates for grounded AI responses with source citations
Multi-strategy document chunking (Recursive, Token, Semantic, and Markdown)
0 GitHub stars
Reranking methods using cross-encoders to improve result precision

ユースケース

Creating factual research tools that provide source citations to minimize hallucinations
Building a Q&A system over proprietary internal company documentation or wikis
Implementing semantic search capabilities for large-scale digital libraries and knowledge bases

概要

主な機能

Integration with leading vector databases including Pinecone, Chroma, and Weaviate
Advanced retrieval patterns including hybrid search, multi-query, and contextual compression
Prompt engineering templates for grounded AI responses with source citations
Multi-strategy document chunking (Recursive, Token, Semantic, and Markdown)
0 GitHub stars
Reranking methods using cross-encoders to improve result precision

ユースケース

Creating factual research tools that provide source citations to minimize hallucinations
Building a Q&A system over proprietary internal company documentation or wikis
Implementing semantic search capabilities for large-scale digital libraries and knowledge bases