What is RAG?

RAG stands for:

Retrieval Augmented Generation

It is a technique used in AI systems where:

• The AI first retrieves information from external sources
• Then it generates an answer using that information

Simple Meaning

Normally, AI models answer questions only from the data they learned during training.

But with RAG:

• The AI can search documents, databases, PDFs, websites, notes, etc.
• It brings relevant information
• Then creates a better and more accurate answer

Real-Life Example

Imagine you ask:

"What are the rules written in my uploaded PDF?"

Without RAG:

• The AI may not know your PDF content

With RAG:

1. The system searches your PDF
2. Finds relevant sections
3. Sends that information to the AI
4. AI generates the final answer

Example from your uploaded file: :contentReference[oaicite:0]

Full Form Breakdown

Retrieval

Finding useful information from external sources.

Examples:

• PDFs
• Notes
• Databases
• Websites
• Company documents

Augmented

The retrieved information is added to the AI prompt/context.

The AI gets "extra knowledge".

Generation

The AI generates the final response using both:

• Its trained knowledge
• Retrieved information

Basic Workflow of RAG

User Question
      ↓
Search Relevant Data
      ↓
Retrieve Important Information
      ↓
Send Information to LLM
      ↓
Generate Final Answer