What is RAG?

Retrieval-Augmented Generation

RAG combines language models with external knowledge bases for accurate, up-to-date responses.

The Three Pillars

Query → Retrieval → Context + Query → LLM → Response

1. Retrieval: Search your knowledge base for relevant documents 2. Augmentation: Combine retrieved context with the query 3. Generation: LLM generates response using the context

Why RAG Matters

| Problem | RAG Solution | |---------|--------------| | Knowledge cutoff | Update knowledge base anytime | | Hallucinations | Ground responses in sources | | No private data access | Index your own documents | | Context limits | Retrieve only what's needed |

Use Cases

• Enterprise Knowledge: Search internal docs, wikis, policies
• Customer Support: Product manuals, troubleshooting guides
• Legal/Compliance: Case law, regulations, contracts
• Research: Academic papers, citations

RAG vs Fine-Tuning

Use RAG when: Information changes, need citations, large private data Use Fine-Tuning when: Changing behavior/style, stable knowledge

→ Proceed to Lesson 2: Vector Databases