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Module 7 of 1613 min read5 sub-lessons Docs view

Module 7: Introduction to RAG (Retrieval Augmented Generation)

Understand what RAG is, why it exists, the 5-step RAG pipeline, where it shines, where it fails, and how to architect one without a framework.

Module 7: Introduction to RAG (Retrieval Augmented Generation)

What this module gives you

Build a complete mental model of RAG: what it is, why it exists, its pipeline, its costs, and its failure modes. By the end you can architect a RAG system on a whiteboard, even before writing code.

Skills you will pick up

  • Defining RAG and the problem it solves
  • Drawing the 5-step RAG pipeline from memory
  • Picking RAG over fine-tuning correctly
  • Spotting RAG failure modes
  • Sketching a production RAG architecture

Why it matters in production

RAG is the dominant 2026 pattern for adding private, fresh, or proprietary knowledge to LLMs. Every "Chat with your X" product uses it. Knowing RAG inside-out is non-negotiable for an AI engineering career.

Lessons in this module

  1. 1

    Lesson 7.1

    The problem RAG solves

    3 min
  2. 2

    Lesson 7.2

    The 5-step RAG pipeline

    2 min
  3. 3

    Lesson 7.3

    RAG vs fine-tuning vs long context

    2 min
  4. 4

    Lesson 7.4

    Where RAG shines, and where it fails

    2 min
  5. 5

    Lesson 7.5

    Sketching a production RAG architecture

    2 min

Recap

You understand RAG end-to-end without writing code. You can recite the pipeline, compare it to alternatives, screen for the right use case, and sketch the architecture. Module 8 makes you fluent in embeddings, the heart of step 3.

Ready to start?

Open Lesson 7.1: The problem RAG solves

Start first lesson