rag-engineer

What problem does it solve?

This Skill helps teams build Retrieval-Augmented Generation (RAG) systems by enabling reliable access to external knowledge during LLM inference, reducing hallucinations and data gaps.

Core Features & Use Cases

• Vector embeddings and similarity search
• Document chunking and preprocessing
• Retrieval pipeline design
• Semantic search implementation
• Context window optimization
• Hybrid search (keyword + semantic)
• Use Case: Build a RAG assistant that answers questions by retrieving relevant documents from a knowledge base.

Quick Start

Load your documents, select an embedding model, initialize a vector store, and run an LLM with a retrieval step.