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Large language models are impressive, but they often produce outdated, unsupported, or inaccurate answers.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.
You will build a fully functional PDF chatbot that allows users to upload documents and ask natural-language questions about their content. You will learn how to extract text from PDFs, preserve page numbers, clean document content, create overlapping text chunks, and return answers with supporting citations.
The course also covers text embeddings, vector search, and vector databases such as ChromaDB and FAISS. You will learn how to convert document chunks into numerical vectors, store those vectors, perform similarity searches, and retrieve information based on meaning instead of exact keyword matches.
As your skills grow, you will explore advanced semantic search techniques, including metadata filtering, similarity thresholds, query rewriting, hybrid search, keyword retrieval, and result reranking. These techniques will help you improve retrieval accuracy and build more reliable AI applications.
You will also create a conversational RAG chatbot that remembers previous messages, understands follow-up questions, retrieves fresh evidence for every response, and clearly separates conversational memory from document knowledge. You will add citations, confidence indicators, insufficient-evidence responses, and practical guardrails to reduce hallucinations and unsupported claims.
For the enterprise section of the course, you will build a multi-document enterprise knowledge assistant for departments such as HR, IT, finance, operations, and compliance. You will organize documents using metadata, create department-specific collections, manage document versions, support multiple file formats, and implement role-based access controls.
You will also learn how to evaluate and improve your RAG system using metrics such as retrieval relevance, groundedness, answer relevance, citation accuracy, and response latency. You will build a RAG evaluation dashboard for testing questions, reviewing retrieved sources, identifying failed answers, and comparing different retrieval configurations.
By the end of the course, you will complete a portfolio-ready Enterprise Knowledge and Research Copilot using Python, Streamlit, embeddings, vector databases, and modern generative AI techniques.
This course is ideal for Python developers, AI beginners, data professionals, freelancers, startup founders, and anyone interested in building AI chatbots that talk to your data.
