RAG

Simple Definition

RAG stands for Retrieval-Augmented Generation. It’s a technique that connects an AI model to external documents or databases so the AI can answer questions using your specific data — not just what it was trained on.

The Problem RAG Solves

Standard AI models like ChatGPT only know what they were trained on, up to their knowledge cutoff date. They don’t know about your company’s internal documents, your product catalog, or your latest research.

RAG solves this by letting the AI retrieve relevant information from your own data sources before generating a response.

How RAG Works (Simply)

1. Your question arrives — you ask the AI something
2. Retrieval — the system searches your documents or database for relevant chunks of information
3. Generation — the AI reads the retrieved information and uses it to generate an accurate, grounded answer
4. Response — you get an answer based on your actual data

Practical Example

Imagine you build a customer support chatbot for your company. Without RAG, the AI would only know general information. With RAG, the AI can:

• Search your knowledge base and FAQs
• Find relevant policy documents
• Access your product documentation
• Answer customer questions accurately based on your actual content

Why RAG Matters

• Accuracy — answers are grounded in actual source documents, not just training data
• Customization — you can give the AI access to your specific knowledge
• Freshness — your data can be updated without retraining the model
• Privacy — your proprietary data doesn’t need to be part of public training

Who Uses RAG

RAG is used by businesses building AI-powered applications including:

• Customer support chatbots
• Internal knowledge base search
• Legal document review tools
• Medical information systems
• Research assistants

Related Terms

• LLM — the AI model that generates the response in a RAG system
• Context Window — the memory limit that RAG helps work around
• AI Agent — often uses RAG for accessing external information