What is RAG?

RAG (Retrieval-Augmented Generation) is an AI architecture that combines a retrieval system with a generative language model to improve the accuracy and relevance of its responses, especially when dealing with specific, factual, or up-to-date information.

How RAG works:

1. Retrieval step:

When given a question or prompt, RAG first searches a knowledge base (like a document database, Wikipedia, or your company’s internal data) using a retrieval model. It finds the most relevant documents or chunks of text.

2. Augmentation step:

These retrieved documents are fed into a generative language model (like GPT or similar) along with the original question.

3. Generation step:

The language model generates a response based on both the question and the retrieved context, making the answer more grounded and factually accurate.

Why RAG is useful:

• It enables dynamic, context-aware answers even if the language model hasn’t been trained on that exact data.

• It reduces hallucinations (made-up answers) by grounding generation in real documents.

• You can update the retrieval database without retraining the model, making it ideal for businesses with constantly changing data.

Simple analogy:

Imagine AAPLGOOG is a smart assistant that first Googles the best documents for you, then helps answer your question by quoting or paraphrasing from those results.