Learn how to build powerful Retrieval-Augmented Generation applications with Python in this practical, project-based course. You will create PDF chatbots, semantic search engines, vector database applications, and a complete enterprise knowledge assistant that can answer questions using your own documents and private data.
Large language models are impressive, but they often produce outdated, unsupported, or inaccurate answers. Retrieval-Augmented Generation, commonly known as RAG, solves this problem by connecting an AI model to external knowledge sources. Instead of depending only on the model’s built-in knowledge, a RAG application retrieves relevant information from your documents and uses that information to generate a more accurate, grounded response.
Throughout this course, you will learn the complete workflow for building RAG chatbots with Python. You will start by understanding the core architecture of a RAG system, including document ingestion, text extraction, chunking, embeddings, retrieval, prompt construction, and answer generation.
