What is RAG and why is it used?

RAG (Retrieval-Augmented Generation) is a technique that provides LLMs with access to specific, private, or updated data outside their original training set to improve the accuracy and relevance of their responses.

How does this skill help reduce AI hallucinations?

By implementing grounding prompts and source-based retrieval, the skill ensures the LLM generates answers based on provided documents rather than internal weights, often including citations for verification.

Which vector databases does this skill support?

The skill includes implementation patterns and configuration guides for major vector databases including Pinecone, Chroma, Weaviate, Milvus, Qdrant, and FAISS.

Does it support complex search methods like Hybrid Search?

Yes, it provides code patterns for Hybrid Search (combining vector similarity with keyword matching), Multi-Query retrieval, and Contextual Compression to maximize search precision.

Advanced RAG Implementation

Name: Advanced RAG Implementation
Author: Cursedpotential

byCursedpotential

Data Science & ML

Builds sophisticated Retrieval-Augmented Generation systems to ground LLMs with external data and private knowledge bases.

About

The RAG Implementation skill provides a comprehensive framework for developing knowledge-aware AI applications by integrating Large Language Models with vector databases and semantic search. It offers standardized patterns for document chunking, embedding generation, and advanced retrieval strategies like hybrid search and re-ranking, ensuring AI responses are factual, contextually relevant, and grounded in proprietary data sources. This skill is essential for developers building production-ready documentation assistants, research tools, or enterprise Q&A systems that require high precision and source transparency.

Key Features

Automated document chunking and semantic splitting strategies
0 GitHub stars
Advanced retrieval patterns including Hybrid and Multi-Query search
Vector database integration for Pinecone, Chroma, and Weaviate
Evaluation frameworks for measuring grounding and retrieval quality
Response reranking using Cross-Encoders for improved accuracy

Use Cases

Building internal Q&A systems for proprietary corporate documentation
Reducing LLM hallucinations by providing real-time factual context
Creating research assistants with automatic source citation and verification

About

Key Features

Automated document chunking and semantic splitting strategies
0 GitHub stars
Advanced retrieval patterns including Hybrid and Multi-Query search
Vector database integration for Pinecone, Chroma, and Weaviate
Evaluation frameworks for measuring grounding and retrieval quality
Response reranking using Cross-Encoders for improved accuracy

Use Cases

Building internal Q&A systems for proprietary corporate documentation
Reducing LLM hallucinations by providing real-time factual context
Creating research assistants with automatic source citation and verification