Part 3: Q&A chatbots
This part marks a shift from summarizing information to answering meaningful questions. Here, you’ll dive into Retrieval-Augmented Generation (RAG)—the core technique that enables LLMs to perform question answering over large or private knowledge bases. You’ll learn how RAG combines three critical components—vector stores, retrievers, and language models—to locate semantically relevant information and synthesize grounded, coherent answers. Rather than relying solely on the model’s internal knowledge, you’ll see how to anchor responses in external data, dramatically improving accuracy and trustworthiness.
You’ll begin by building the essential pieces of a RAG system from scratch to fully understand how they work together. This hands-on approach will give you an intuitive sense of how documents are ingested, embedded, and retrieved using semantic similarity rather than keyword matching. Once you’ve mastered the fundamentals, you’ll take things further by introducing LangChain’s modular RAG components, which streamline development and make complex pipelines easier to maintain. You’ll also incorporate LangSmith to monitor, trace, and debug every step of your chatbot’s reasoning process—ensuring visibility into how answers are formed and why certain results are chosen.