What Is Retrieval Augmented Generation (RAG)?

RAG is the technology that makes AI assistants actually useful for specific domains.

The Problem RAG Solves

Large Language Models (LLMs) like GPT-4 have a problem:

• Training data has a cutoff date
• They don't know your specific information
• They can hallucinate (make things up)

RAG solves this by giving the AI relevant information before it responds.

How RAG Works

1. Document Processing

Your documents are split into chunks and converted to vectors (numbers that capture meaning).

2. Storage

These vectors are stored in a vector database for fast retrieval.

3. Query

When a user asks a question:

• The question is converted to a vector
• Similar content is retrieved
• Relevant chunks are found

4. Generation

The LLM receives:

• The user's question
• Retrieved relevant content
• Instructions on how to respond

5. Response

The AI generates an answer based on your actual content, not just its training data.

Why RAG Matters

Without RAG:

• Generic answers
• Potential hallucinations
• No source attribution

With RAG:

• Specific, accurate answers
• Grounded in your content
• Can cite sources

RAG in Practice

Assisters uses RAG under the hood:

1. You upload documents
2. We process and store them
3. User asks a question
4. We retrieve relevant content
5. AI generates an accurate answer

You get the benefits without building the infrastructure.

RAG is what makes AI assistants actually know things.

See RAG in Action →