Your First RAG System

What is RAG?

Retrieval-Augmented Generation (RAG) is a pattern that gives an LLM access to external knowledge at query time. Instead of relying only on what the model memorized during training, RAG retrieves relevant documents on demand and passes them into the prompt — so the model can answer about your data, fresh data, and private data.

The Three Problems RAG Solves

1. Hallucination — Without grounding, models guess plausibly-wrong answers about specific entities.
2. Stale knowledge — A model trained in 2024 doesn't know about events from yesterday.
3. Private data — Internal docs, customer records, codebases were never in the training set.

The Core Loop

                  ┌────────────┐
question ───────▶│   embed    │──▶ query vector
                  └────────────┘
                                       │
                                       ▼
                              ┌─────────────────┐
                              │  vector store   │ ──▶ top-K chunks
                              └─────────────────┘
                                       │
                                       ▼
                          ┌─────────────────────────┐
                          │ build prompt with       │
                          │   system + chunks + Q   │
                          └─────────────────────────┘
                                       │
                                       ▼
                                  ┌────────┐
                                  │  LLM   │ ──▶ grounded answer
                                  └────────┘

When to RAG vs. Fine-Tune

Use case	RAG	Fine-tune
Answer questions about a knowledge base	✓
Frequent content updates	✓
Enforce a tone/format/style		✓
Specialize for a narrow domain task	weak	✓
Combine multiple sources at query time	✓	weak

A common production pattern is both: fine-tune for style and behavior; RAG for fresh, citable facts.

RAG Isn't Magic

It still relies on:

• Good chunking (Day 5, section 2)
• Good embeddings (Day 2)
• Sometimes a knowledge graph for structured relationships (Day 3)
• An LLM smart enough to integrate the retrieved context (Day 4)

This is the capstone because RAG braids everything you've learned.