Does it include advanced retrieval patterns?

Yes, it covers sophisticated patterns such as hybrid search (BM25 + Dense), multi-query retrieval, contextual compression, and parent document retrieval.

What vector databases are supported by this skill?

This skill provides implementation patterns for a wide range of vector stores including Pinecone, Weaviate, Milvus, Chroma, Qdrant, and FAISS.

How does this skill help reduce LLM hallucinations?

It implements grounding techniques by retrieving relevant context from external knowledge bases and using specific prompt engineering to ensure the LLM stays within that context.

Can I use this for production-grade AI applications?

Absolutely. It includes best practices for chunking strategies, metadata filtering, reranking, and evaluation metrics necessary for scaling RAG in production.

RAG Implementation Expert

Name: RAG Implementation Expert
Author: EngineerWithAI

byEngineerWithAI

Data Science & ML

Builds production-grade Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search for LLM applications.

About

This skill empowers developers to bridge the gap between Large Language Models and proprietary data by implementing robust Retrieval-Augmented Generation (RAG) pipelines. It provides comprehensive guidance on document ingestion, advanced chunking strategies, vector database integration, and sophisticated retrieval methods like hybrid search and reranking. Whether you are building a documentation assistant, a research tool, or a factual Q&A system, this skill ensures your AI applications remain grounded, accurate, and capable of providing verifiable source citations while minimizing hallucinations.

Key Features

0 GitHub stars
Advanced document chunking and metadata strategies
Hybrid and multi-query retrieval optimization techniques
Cross-encoder reranking for superior result relevance
Integration with major vector databases like Pinecone, Weaviate, and Chroma
Comprehensive RAG evaluation and monitoring patterns

Use Cases

Creating research tools with accurate source citations and grounding
Building Q&A systems over proprietary internal documentation
Developing chatbots with real-time access to domain-specific knowledge

About

Key Features

0 GitHub stars
Advanced document chunking and metadata strategies
Hybrid and multi-query retrieval optimization techniques
Cross-encoder reranking for superior result relevance
Integration with major vector databases like Pinecone, Weaviate, and Chroma
Comprehensive RAG evaluation and monitoring patterns

Use Cases

Creating research tools with accurate source citations and grounding
Building Q&A systems over proprietary internal documentation
Developing chatbots with real-time access to domain-specific knowledge