Which vector databases can I use with this skill?

This skill provides implementation patterns for Pinecone, Weaviate, Milvus, Chroma, Qdrant, and Meta's FAISS library.

How does this skill help reduce AI hallucinations?

By implementing retrieval-grounded prompts and source citations, the skill ensures the LLM relies on provided context rather than its training data for factual claims.

What is Retrieval-Augmented Generation (RAG)?

RAG is an architectural pattern that provides LLMs with external, authoritative data to generate more accurate, context-aware, and factually grounded responses.

Does this support local development?

Yes, it includes configurations for local vector stores like Chroma and open-source embedding models that can run on-premise.

What retrieval strategies are included?

The skill covers dense retrieval, sparse retrieval (BM25), hybrid search, multi-query variations, and reranking using cross-encoders.

RAG System Implementation

Name: RAG System Implementation
Author: Activer007

byActiver007

Data Science & ML

Build Retrieval-Augmented Generation (RAG) systems to ground LLM applications in external knowledge and proprietary data.

About

This skill provides a comprehensive framework for implementing Retrieval-Augmented Generation (RAG) to enhance LLM applications with domain-specific knowledge. It covers the entire pipeline from document ingestion and semantic chunking to vector database integration and advanced retrieval strategies like hybrid search and reranking. Whether you are building a proprietary Q&A system, a documentation assistant, or a research tool, this skill offers production-grade patterns to minimize hallucinations and ensure factual accuracy using industry-standard tools like LangChain and various vector stores.

Key Features

Evaluation frameworks to measure system accuracy and groundedness
Support for major vector databases including Pinecone, Chroma, and Weaviate
Advanced retrieval patterns like Hybrid Search and Contextual Compression
Integration of reranking models to improve result relevance and precision
Sophisticated document chunking strategies including semantic and recursive splitting
0 GitHub stars

Use Cases

Building internal Q&A systems over private company documentation and wikis
Creating factual chatbots with access to domain-specific or real-time data
Implementing semantic search engines for complex natural language queries

About

Key Features

Evaluation frameworks to measure system accuracy and groundedness
Support for major vector databases including Pinecone, Chroma, and Weaviate
Advanced retrieval patterns like Hybrid Search and Contextual Compression
Integration of reranking models to improve result relevance and precision
Sophisticated document chunking strategies including semantic and recursive splitting
0 GitHub stars

Use Cases

Building internal Q&A systems over private company documentation and wikis
Creating factual chatbots with access to domain-specific or real-time data
Implementing semantic search engines for complex natural language queries