slug: rag-fundamentals
title: "RAG — Retrieval-Augmented Generation for Grounded AI"
version: 1.0.0
description: "Ground LLMs with your data using retrieval, embeddings, vector databases, and the full RAG pipeline."
category: ai-and-llm
tags: [rag, retrieval, embeddings, vector-database, grounding, knowledge-base]
difficulty: intermediate

xp:
  read: 15
  walkthrough: 40
  exercise: 25
  quiz: 20
  quiz-perfect-bonus: 10

time:
  quick: 5
  read: 20
  guided: 50

prerequisites: [llm-fundamentals]
related: [ai-agents, prompt-engineering]

triggers:
  - "What is RAG?"
  - "How do I ground AI with my own data?"
  - "What are embeddings?"
  - "How do vector databases work?"

visuals:
  diagrams: [diagram-mermaid, diagram-architecture]
  quiz-types: [quiz-drag-order, quiz-matching]
  playground: bash
  slides: true

sources:
  - url: "https://docs.langchain.com"
    label: "LangChain Documentation"
    type: docs
  - url: "https://docs.llamaindex.ai"
    label: "LlamaIndex Documentation"
    type: docs
  - url: "https://www.pinecone.io/learn"
    label: "Pinecone Learning Center"
    type: docs