Why is RAG used?

RAG is used to help a language model answer questions using relevant external information instead of relying only on what it learned during training.

This is useful because a model's built-in knowledge can be incomplete, outdated, too general, or missing private information such as company documents.

Why use RAG?

RAG is commonly used to:

• Access up-to-date information — retrieve newer information without retraining the model.
• Use private or domain-specific knowledge — answer from internal documents, manuals, policies, product data, or other controlled sources.
• Reduce unsupported answers — give the model relevant evidence to use when generating a response.
• Handle large knowledge collections — retrieve only the most relevant information instead of placing an entire document library in the prompt.
• Update knowledge more easily — change the external knowledge base without modifying the underlying language model.

Simple example

Suppose a company has hundreds of product manuals.

Without RAG, a language model may not know the details of a specific product or may rely on outdated information.

With RAG, the system can retrieve the relevant sections of the latest manual and provide them to the model before it answers.

RAG is therefore especially useful when the answer should depend on specific, current, or private information.

However, RAG does not automatically make every answer correct. Its quality depends heavily on whether the system retrieves the right information and whether the model uses that information correctly.

For the basic retrieve → augment → generate flow, see “What is Retrieval-Augmented Generation (RAG)?”.

In short, RAG is used to give generative models access to relevant external knowledge so their answers can be more current, specific, and grounded.