What is Retrieval-Augmented Generation (RAG)?

In the previous tutorial, we explored Cache-Augmented Generation (CAG), a powerful technique for injecting knowledge directly into a model's context. But this approach has a hard limit: the model's context window. Even with massive, one-million-token contexts, research and practical application show that models suffer from imperfect recall; details placed in the middle of a long prompt can be easily missed or ignored. Furthermore, from a practical engineering standpoint, sending your entire company's knowledge base - thousands of documents or millions of database rows in every single prompt is unworkable due to prohibitive latency and cost. We need a more scalable, efficient, and reliable method to ground LLMs in vast external knowledge bases.

This is the problem that Retrieval-Augmented Generation (RAG) is designed to solve. Instead of handing the LLM the entire library for every question, RAG acts as an expert librarian. It first retrieves only the most relevant snippets of information from the knowledge base based on the user's query. It then augments the model's prompt with just this targeted context, allowing the LLM to generate an answer grounded in precise, verifiable facts. This approach drastically reduces latency, lowers costs, and, by providing focused context, often improves the accuracy and relevance of the final response.

In this tutorial, you will master the fundamentals of RAG by building a system from the ground up. First, you will implement a simple RAG from first principles using basic Python libraries to solidify your understanding of the core mechanics. You will then refactor this system using the robust components of the LangChain framework, package it into a production-ready API with FastAPI, and finally, containerize the entire application with Docker, making it ready for real-world deployment.