Which vector databases does this skill support?

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

What retrieval strategies are included?

The skill covers dense retrieval, sparse retrieval (BM25), hybrid search, multi-query expansion, and hypothetical document embeddings (HyDE).

Does this skill help with LLM hallucinations?

Yes, it focuses on groundedness and source citation, ensuring AI responses are strictly based on the retrieved context from your own documents.

What is RAG in AI development?

Retrieval-Augmented Generation (RAG) is a technique that provides LLMs with specific, external data to improve response accuracy and reduce hallucinations.

RAG Systems Implementation

Name: RAG Systems Implementation
Author: Activer007

byActiver007

Data Science & ML

Architects and implements robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses.

About

This skill provides a comprehensive framework for building high-performance RAG systems that connect Large Language Models to your proprietary data. It guides developers through the entire pipeline, including document ingestion, advanced chunking strategies, embedding model selection, and vector database integration. By implementing sophisticated retrieval patterns like hybrid search, multi-query expansion, and cross-encoder reranking, this skill helps create AI applications that are accurate, verifiable, and free from hallucinations.

Key Features

Contextual compression and cross-encoder reranking techniques
Hybrid search and multi-query retrieval pattern implementations
0 GitHub stars
Advanced document chunking and semantic splitting strategies
RAG evaluation frameworks for accuracy and groundedness metrics
Vector database configuration for Pinecone, Weaviate, Chroma, and more

Use Cases

Reducing LLM hallucinations by grounding responses in real-time external data
Building proprietary document Q&A systems for enterprise knowledge bases
Creating research assistants that provide verifiable source citations

About

Key Features

Contextual compression and cross-encoder reranking techniques
Hybrid search and multi-query retrieval pattern implementations
0 GitHub stars
Advanced document chunking and semantic splitting strategies
RAG evaluation frameworks for accuracy and groundedness metrics
Vector database configuration for Pinecone, Weaviate, Chroma, and more

Use Cases

Reducing LLM hallucinations by grounding responses in real-time external data
Building proprietary document Q&A systems for enterprise knowledge bases
Creating research assistants that provide verifiable source citations