RAG Architecture Skill

Build retrieval-augmented generation systems that ground LLMs in your data.

Last validated: April 2026 (text-embedding-3 family, contextual retrieval patterns)

Core Principle

RAG = Retrieval + Generation. Instead of relying solely on the model's training data, retrieve relevant context at query time and include it in the prompt. This reduces hallucination and enables access to private/current data.

RAG Pipeline

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Query     │────▶│   Embed     │────▶│  Retrieve   │────▶│   Augment   │
│  "How do I  │     │  Query to   │     │  Top-K      │     │  Add to     │
│   deploy?"  │     │  Vector     │     │  Documents  │     │  Prompt     │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘