What vector databases does this skill cover?

The skill provides implementation patterns and configuration guides for popular databases including Pinecone, Weaviate, Milvus, Chroma, Qdrant, and FAISS.

Does this skill support advanced retrieval techniques?

Yes, it includes patterns for Hybrid Search (combining keyword and semantic search), Multi-Query Retrieval, Contextual Compression, and Parent Document Retrieval.

How does RAG help reduce LLM hallucinations?

By providing specific, retrieved context from your own documents to the LLM, the model is instructed to answer based only on that data, significantly reducing the chance of fabricated information.

How do I choose the right chunking strategy?

This skill guides you through Recursive Character, Token-based, Semantic, and Markdown-specific splitting to help you balance context preservation with retrieval precision.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: GaitanS

byGaitanS

数据科学与机器学习

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

关于

This skill empowers developers to architect and implement full-stack RAG pipelines, bridging the gap between large language models and private or domain-specific datasets. It provides expert guidance on vector database selection, embedding models, advanced retrieval strategies like hybrid search and reranking, and specialized document chunking methodologies. Whether building a corporate knowledge base assistant or a high-accuracy research tool, this skill ensures grounded, factually accurate responses while providing the tools to minimize hallucinations through structured evaluation and source citations.

主要功能

Comprehensive implementation patterns for vector databases like Pinecone, Chroma, and Weaviate
Reranking integration using Cross-Encoders and LLM-based scoring
Advanced retrieval strategies including Hybrid Search and Multi-Query variations
Optimized document chunking techniques including semantic and recursive splitting
0 GitHub stars
Evaluation frameworks for measuring accuracy, groundedness, and retrieval quality

使用场景

Creating documentation assistants that access live, domain-specific knowledge bases
Reducing model hallucinations by grounding AI responses in verified internal facts
Building proprietary document Q&A systems with verifiable source citations

关于

主要功能

Comprehensive implementation patterns for vector databases like Pinecone, Chroma, and Weaviate
Reranking integration using Cross-Encoders and LLM-based scoring
Advanced retrieval strategies including Hybrid Search and Multi-Query variations
Optimized document chunking techniques including semantic and recursive splitting
0 GitHub stars
Evaluation frameworks for measuring accuracy, groundedness, and retrieval quality

使用场景

Creating documentation assistants that access live, domain-specific knowledge bases
Reducing model hallucinations by grounding AI responses in verified internal facts
Building proprietary document Q&A systems with verifiable source citations