Introduction

RAG means Retrieval-Augmented Generation. It's a way that combines two main skills to help AI systems give better answers.

What is RAG?

At its core, RAG is about letting AI systems use outside information to create responses.

The Problem RAG Solves

AI models are trained on vast amounts of text data, but we have limitations:

1. Knowledge has a cutoff date (Claude’s is October 2024)

2. Might not have been trained on specialized information you need

3. Can sometimes generate incorrect information

The Two Components of RAG

RAG combines two processes:

Retrieval: Finding relevant information from a knowledge source (like documents, websites, or databases)

Generation: Using that retrieved information to create a helpful response

The RAG Process Step-by-Step

1. Your question is received

2. The system searches through a knowledge base for relevant information

3. The most useful pieces of information are selected

4. The AI model receives both your question AND the retrieved information

5. Model generates responsed based on its training and this additional knowledge

Why RAG Matters

RAG helps solve several basic problems:

• It gives access to more current information

• It can include specialized knowledge

• It reduces wrong information by using specific sources

• It can show where the information came from, making it more transparent

Real-World Applications

Examples:

• Customer support systems needing detailed product knowledge

• Enterprise search tools accessing company information

• Personal assistants that can check your documents

• Research tools exploring scientific literature

RAG represents a shift from AI systems that rely solely on their training to those that can actively search for and use external information to craft responses.