Expanding Knowledge with Retrieval-Augmented Generation (RAG)
Explore how retrieval-augmented generation (RAG) improves large language models by using external knowledge to answer questions accurately. Understand the RAG pipeline including document chunking, embedding, storing vectors in a database, and real-time retrieval to enhance LLM responses with relevant context. This lesson enables you to implement RAG to overcome limitations of static model knowledge and improve application reliability.
We know that a large language model, for all its brilliance, has two fundamental limitations: its knowledge is frozen in time, and it is ignorant of your private, specific data. By default, any question we ask an LLM is like a “closed-book exam.” It must answer from memory. This is the primary source of hallucinations and its inability to answer questions about recent events or proprietary information.
What if we could change the rules of the exam? This is the elegant idea behind retrieval-augmented generation (RAG), the single most important pattern in modern LLM application development. Instead of asking the model to answer from memory, we give it access to new data, like in an “open-book exam.” The tool we use to find the right page in the book at the right time is precisely the engine we just explored in our last lesson: semantic search powered by embeddings.
In this lesson, we will explore the complete RAG pipeline, from preparing our library of documents to retrieving the perfect piece of context for any user query.
Retrieval-augmented generation
The core idea is simple and elegant. When a user asks a question, we first ...