Explainer: Retrieval Augmented Generation (RAG) for AI models

Retrieval Augmented Generation (RAG) is a technique that enhances AI models by fetching relevant information from external knowledge sources when answering a question. It works by converting both the user's query and stored documents into numerical vectors, then finding and retrieving the most similar document chunks. These retrieved chunks are then inserted into the prompt as context for the model, allowing it to generate more accurate, up-to-date answers without retraining. A practical example demonstrates using a simple vectorization technique to build a working RAG system that can be run with an Anthropic API key.