rag-architecturelisted

# 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 │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ ▼ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Answer │◀────│ Generate │◀────│ Format │◀────│ Context │ │ Grounded │ │ With LLM │ │ Prompt │ │ + Query │ │ Response │ │ │ │ │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ ``` ## Indexing Pipeline ### Document Processing ``` ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │