Does this support local development?

Yes, it includes configurations for local vector stores like Chroma and FAISS, as well as open-source embedding models that can run on your own hardware.

Which vector databases are supported by this skill?

The skill provides implementation patterns for major providers including Pinecone, Weaviate, Milvus, Chroma, Qdrant, and FAISS.

What is RAG and why should I use it?

RAG stands for Retrieval-Augmented Generation. It allows LLMs to access specific, relevant data from your own documents, leading to more accurate, fact-based answers and fewer hallucinations.

How does this skill help reduce AI hallucinations?

By implementing retrieval patterns that force the LLM to ground its answers in retrieved context and provide citations, the likelihood of the model making up information is significantly reduced.

What advanced retrieval techniques are included?

It covers hybrid search (combining keyword and semantic search), reranking with cross-encoders, multi-query generation, and contextual compression.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: carloscroc

bycarloscroc

데이터 과학 및 ML

Builds advanced Retrieval-Augmented Generation (RAG) systems to ground LLM responses with external document knowledge and vector search.

소개

This skill provides a comprehensive framework for implementing Retrieval-Augmented Generation (RAG) within LLM applications. It guides developers through the entire pipeline, including document ingestion, semantic chunking, vector database integration, and advanced retrieval strategies like hybrid search and reranking. By following these patterns, developers can create AI systems that provide factually accurate, source-cited responses while significantly reducing hallucinations and improving context-aware performance across proprietary datasets.

주요 기능

Advanced vector database integration (Pinecone, Weaviate, Chroma, FAISS)
0 GitHub stars
Contextual compression and reranking for optimized search results
Comprehensive RAG evaluation metrics for accuracy and groundedness
Sophisticated document chunking and semantic splitting strategies
Implementation of hybrid search and multi-query retrieval patterns

사용 사례

Implementing semantic search for large-scale enterprise documentation
Building proprietary document Q&A systems and research tools
Creating factual chatbots with real-time access to external knowledge

소개

주요 기능

Advanced vector database integration (Pinecone, Weaviate, Chroma, FAISS)
0 GitHub stars
Contextual compression and reranking for optimized search results
Comprehensive RAG evaluation metrics for accuracy and groundedness
Sophisticated document chunking and semantic splitting strategies
Implementation of hybrid search and multi-query retrieval patterns

사용 사례

Implementing semantic search for large-scale enterprise documentation
Building proprietary document Q&A systems and research tools
Creating factual chatbots with real-time access to external knowledge