What is RAG (Retrieval-Augmented Generation)?

The problem with pure language models

Large language models like GPT-4 and Claude are trained on data up to a certain date. They know nothing about events after that date and also cannot answer questions about your internal documents, products, or procedures — unless you repeat that information in every prompt.

Moreover, language models can hallucinate: producing plausibly sounding but incorrect information. This is dangerous in professional contexts.

What is RAG?

Retrieval-Augmented Generation (RAG) solves this by connecting the language model to an external knowledge source. For each question, relevant information is first retrieved from a database or document collection, and that information is given to the model together with the question.

Illustration created with Canva AI

How does RAG work step by step?

1. Indexing — Documents are split into pieces (chunks) and converted to vector representations via an embedding model
2. Retrieval — For a new question, a similar vector search is performed to find the most relevant documents
3. Generation — The found documents are given to the language model together with the question as context
4. Answer — The model generates an answer based on the provided context, not on training data

Advantages of RAG

• Current information — The knowledge source can be updated in real time without retraining the model
• Fewer hallucinations — The model relies on concrete sources that can be cited
• Own data — Works with internal documents, manuals, contracts, or knowledge bases
• Transparency — Answers can be accompanied by source references

Applications

• Customer service chatbots that work with product documentation
• Legal AI that can consult contracts and legislation
• Medical AI that can look up current guidelines
• Internal knowledge management tools

RAG vs. fine-tuning

A frequently asked question is: when do you choose RAG and when fine-tuning? RAG is suitable when the knowledge source changes regularly or when you need source references. Fine-tuning is better when you want to structurally adjust the behavior or tone of the model.