Can I use this for production-grade RAG?

Absolutely. It includes patterns for reranking, metadata filtering, and evaluation metrics essential for production reliability.

What chunking strategies are available?

It covers recursive character splitting, token-based splitting, semantic chunking, and Markdown header-based segmentation.

What vector databases are supported by this skill?

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

How does this skill help prevent LLM hallucinations?

It implements grounded response patterns where the LLM is constrained to provide answers only based on retrieved context and include source citations.

Does it support hybrid search?

Yes, it includes advanced patterns for combining dense (semantic) retrieval with sparse (keyword/BM25) retrieval for better accuracy.

RAG Implementation Expert

Name: RAG Implementation Expert
Author: as4584

byas4584

Data Science & ML

Build robust Retrieval-Augmented Generation systems for LLM applications using vector databases and semantic search.

About

This skill empowers developers to bridge the gap between Large Language Models and external knowledge bases by implementing high-performance RAG architectures. It provides comprehensive guidance on document chunking, embedding generation, vector storage, and advanced retrieval patterns like hybrid search and reranking. Use this skill to minimize hallucinations and enable LLMs to provide grounded, source-cited responses based on proprietary or domain-specific data, making it essential for building production-grade AI search and Q&A applications.

Key Features

0 GitHub stars
Contextual compression and reranking techniques to improve answer precision
Standardized prompt engineering templates for grounded AI responses with citations
Implementation of hybrid search patterns combining semantic and keyword retrieval
Support for major vector databases including Pinecone, Chroma, Weaviate, and Milvus
Advanced document chunking strategies including recursive, semantic, and markdown splitting

Use Cases

Developing a Q&A system for proprietary company documentation and wikis
Implementing high-performance semantic search for internal knowledge management
Building research tools that provide factual answers with verifiable source citations

About

Key Features

0 GitHub stars
Contextual compression and reranking techniques to improve answer precision
Standardized prompt engineering templates for grounded AI responses with citations
Implementation of hybrid search patterns combining semantic and keyword retrieval
Support for major vector databases including Pinecone, Chroma, Weaviate, and Milvus
Advanced document chunking strategies including recursive, semantic, and markdown splitting

Use Cases

Developing a Q&A system for proprietary company documentation and wikis
Implementing high-performance semantic search for internal knowledge management
Building research tools that provide factual answers with verifiable source citations