RAG — Retrieval-Augmented Generation, Explained

Overview

RAG (Retrieval-Augmented Generation) enhances language models by retrieving real, relevant documents and incorporating them as context during query time. This process grounds the model in current, specific, and checkable information, overcoming the limitations of its fixed training knowledge without requiring retraining.

In this video

• A language model's knowledge is fixed at training time, lacking access to private or freshly-updated data, and it may confidently invent false answers.
• RAG grounds language models in real, retrievable text, making answers current, specific, and checkable without retraining any model weights.
• The offline RAG process involves splitting documents into small chunks, embedding each chunk into a vector, and storing these vectors in a vector database.
• During live retrieval, a user's question is embedded, and the vector store quickly returns the top few most semantically similar document chunks.
• Retrieved chunks are inserted directly into the prompt alongside the user's question, providing the context for the language model to generate its answer.
• Effective chunking is critical; too coarse or too fine splitting, or insufficient overlap, can negatively impact the quality of information retrieval.
• RAG enables showing citations, significantly reduces hallucination, and preserves data privacy by ensuring documents never enter the model's training.
• Updating the RAG index instantly provides the AI with new information, eliminating the need for model retraining or waiting periods.
• Unlike fine-tuning, which changes model weights for style or skill, RAG supplies dynamic knowledge at query time, making it ideal for changing facts or private corpora.
• Many production AI systems utilize both fine-tuning to shape model behavior and RAG to provide specific, current knowledge.

Frequently asked questions

What problem does RAG solve for AI?

RAG solves the problem of language models having outdated or no access to specific data, which can lead to invented or false answers. It grounds the model in real, retrievable text, making answers current, specific, and checkable without retraining.

How does RAG build its knowledge base?

RAG builds its knowledge base by first splitting documents into small chunks. Each chunk is then processed by an embedding model to create a vector, which is stored in a vector database to form a searchable index.

How does RAG retrieve information for a user's question?

When a user asks a question, it is embedded into a vector space. The RAG system then uses semantic search to find and retrieve the most semantically similar chunks from its vector database in milliseconds.

What are the main benefits of using RAG?

The main benefits of RAG include the ability to provide citations, significantly reduce AI hallucination, and maintain data privacy as documents don't train the model. Additionally, updating the index instantly provides the AI with new information.

How does RAG differ from fine-tuning a language model?

Fine-tuning adjusts a language model's weights to instill a particular style or skill, while RAG provides specific knowledge at the time of the query. RAG is generally preferred for facts that change frequently or for private datasets due to being cheaper, current, and auditable.