Large Language Models (LLMs) have shown significant capabilities in understanding and generating human-like text. However, their knowledge is typically frozen at the time of training, leading to potential inaccuracies with rapidly evolving information or topics not covered in their training data. They can also sometimes generate responses that sound convincing but are factually incorrect, often referred to as hallucinations.

Retrieve-Augmented Generation (RAG) provides a method to address these limitations. It works by integrating an information retrieval step before the generation step. Instead of relying solely on its internal learned parameters, the LLM is provided with relevant context fetched from an external knowledge source to inform its response.

This chapter introduces the fundamental concepts of RAG. We will cover:

• The specific challenges faced by standard LLMs that RAG aims to solve.
• A clear definition of Retrieve-Augmented Generation and its operational flow.
• The essential architectural components: the retriever and the generator.
• How RAG compares to fine-tuning as a method for adapting LLMs.
• The key advantages of using RAG in applications requiring up-to-date or specialized knowledge.

By the end of this chapter, you will understand the motivation for RAG and its basic structure, setting the foundation for subsequent chapters that detail its components and implementation.