Large Language Models possess extensive general knowledge, but this knowledge is typically frozen at the time of their training. They lack access to real time information, internal company documents, or specific datasets created after their training concluded. This limitation restricts their usefulness in applications requiring current or proprietary context.

This chapter introduces Retrieval Augmented Generation (RAG), a technique designed to address this gap. RAG enables LLMs to access and utilize information from external sources dynamically. Instead of relying solely on their internal parameters, models can incorporate relevant, retrieved data into their generation process.

You will learn the core components and workflow of a RAG system. We will cover:

• The concept behind Retrieval Augmented Generation and why it's needed.
• Methods for loading and preparing external documents for retrieval (splitting).
• How text embedding models convert text into searchable vector representations.
• The role of vector stores in indexing and querying these embeddings.
• Implementing the retrieval process to find relevant information based on a user's query.
• Techniques for augmenting the LLM's prompt with the retrieved context.
• Building a basic RAG pipeline step by step.

By the end of this chapter, you will understand how to connect LLMs to external knowledge sources, significantly expanding their applicability for tasks like question answering over specific documents or accessing up to date information.