Which vector databases does this skill support?

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

What is RAG in LLM application development?

RAG, or Retrieval-Augmented Generation, is a technique that retrieves relevant information from an external knowledge base to ground an LLM's response, ensuring accuracy and reducing hallucinations.

Does it provide guidance on document chunking?

Absolutely. It includes strategies for recursive character splitting, token-based splitting, and semantic chunking to preserve context across document segments.

How does this skill improve retrieval quality?

It covers advanced techniques like hybrid search (combining keyword and semantic results), reranking with cross-encoders, and multi-query retrieval to find the most relevant context.

Can I use this for local AI development?

Yes, the skill includes configurations for local vector stores like Chroma and lightweight embedding models from Sentence Transformers.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: kivilaid

bykivilaid

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데이터 과학 및 ML

Builds sophisticated Retrieval-Augmented Generation systems to ground LLM responses with external knowledge and semantic search.

소개

This skill empowers developers to architect and implement production-grade RAG pipelines, enabling LLMs to provide accurate, factual responses based on proprietary or domain-specific data. It covers the entire technical lifecycle—from document chunking and embedding generation to advanced retrieval strategies like hybrid search and reranking. By integrating popular vector databases such as Pinecone, Chroma, and Weaviate with optimized prompt engineering, this skill ensures your AI applications minimize hallucinations and maintain high context relevance for complex enterprise use cases.

주요 기능

Grounded prompt engineering with citation and confidence scoring
Optimized document chunking and semantic splitting techniques
Vector database integration patterns for Pinecone, Chroma, and Weaviate
Advanced retrieval strategies including hybrid and multi-query search
Reranking implementation using cross-encoders for precision
6 GitHub stars

사용 사례

Building Q&A systems over proprietary internal documentation
Creating factual AI chatbots with real-time external knowledge
Implementing semantic search for research and discovery tools

소개

주요 기능

Grounded prompt engineering with citation and confidence scoring
Optimized document chunking and semantic splitting techniques
Vector database integration patterns for Pinecone, Chroma, and Weaviate
Advanced retrieval strategies including hybrid and multi-query search
Reranking implementation using cross-encoders for precision
6 GitHub stars

사용 사례

Building Q&A systems over proprietary internal documentation
Creating factual AI chatbots with real-time external knowledge
Implementing semantic search for research and discovery tools