Introduction to RAG with LlamaIndex

The field of generative AI is constantly evolving, and one of the most exciting recent advancements is the rise of the retrieval-augmented generation (RAG).

What is RAG?

Retrieval-augmented generation (RAG) is a technique that combines traditional machine learning methods (like information retrieval) with LLMs to improve the quality and relevance of generated text. Instead of relying solely on pre-existing knowledge encoded within its parameters, a RAG system actively retrieves relevant information from an external knowledge base before generating a response. This two-step process allows for enhanced factuality as RAG models are less prone to “hallucinating” information. Since their responses are grounded in retrieved facts, they provide more accurate and trustworthy output. The information they provide can also be more up-to-date as RAG systems can access constantly updated databases.