What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation (RAG) is a design pattern that combines the power of large language models with external knowledge retrieval to produce accurate, grounded, and verifiable responses.

The Problem RAG Solves

Large language models are trained on massive datasets, but they have fundamental limitations:

• Static Knowledge: Training data has a cutoff date
• No Access to Private Data: Cannot reason over your internal documents
• Hallucinations: May generate plausible-sounding but incorrect information
• Generic Responses: Lack domain-specific or personalized context

How RAG Works

graph LR
    A[User Query] --> B[Retrieval System]
    B --> C[Vector Database]
    C --> D[Relevant Documents]
    D --> E[LLM with Context]
    E --> F[Grounded Response]
    
    style A fill:#e1f5ff
    style F fill:#d4edda

RAG follows a simple but powerful workflow:

1. Query: User asks a question
2. Retrieval: System searches a knowledge base for relevant information
3. Augmentation: Retrieved context is added to the LLM prompt
4. Generation: LLM produces a response grounded in the retrieved facts

RAG vs. Pure LLM Prompting

Aspect	Pure LLM	RAG
Knowledge Source	Training data only	Training data + external knowledge
Accuracy	Limited by training cutoff	Up-to-date and domain-specific
Verifiability	Difficult to verify	Can cite sources
Cost	Lower per query	Higher (retrieval + generation)
Customization	Requires fine-tuning	Add new documents anytime

Key Components

A RAG system consists of:

1. Knowledge Base: Documents, databases, or structured data
2. Embedding Model: Converts text to vector representations
3. Vector Store: Indexes and retrieves similar content efficiently
4. LLM: Generates responses using retrieved context
5. Orchestration Layer: Coordinates retrieval and generation

Why RAG Matters

RAG enables:

• Factual Accuracy: Responses grounded in real data
• Source Attribution: Track where information came from
• Dynamic Knowledge: Update knowledge without retraining
• Domain Expertise: Specialize models for specific industries
• Privacy: Keep sensitive data out of model training

Real-World Example

Without RAG:

User: "What is our current return policy?"
LLM: "I don't have access to your specific return policy..."

With RAG:

User: "What is our current return policy?"
System retrieves: [policy_document.pdf, section 3.2]
LLM: "According to your return policy (updated Nov 2025), 
     customers have 30 days for full refunds..."

The Evolution to Multimodal RAG

Traditional RAG focused on text documents. Multimodal RAG extends this to:

• PDFs with images and tables
• Audio transcripts and recordings
• Video content and presentations
• Spreadsheets and structured data
• Diagrams and screenshots

This course teaches you to build production-grade multimodal RAG systems that can reason over any data type.