Build Real-World, Enterprise-grade RAG systems – not just toy demos. Large Language Models (LLMs) like ChatGPT are powerful – but on their own they don’t know your company’s documents, policies or reports. That’s where Retrieval Augmented Generation (RAG) comes in. In this course you’ll learn, step by step, how to build professional, fully customizable RAG Applications in Python using LangChain, LangGraph, OpenAI and Chroma – tailored to internal Business Data, Knowledge and Documents.
You won’t just copy a toy example and get “some” result – you’ll understand every Building Block: Loading and Chunking Documents, Embeddings, Vector Databases, Retrieval Strategies, Summarization methods, Conversational Memory, and automated Updates for your Vector Store. By the end, you’ll be able to design, adapt and extend your own Enterprise RAG Pipelines with Confidence.
What makes this course different?
Most RAG tutorials stop after a simple “ask questions about this PDF” demo. This course goes several levels deeper:
- RAG inside a larger, agentic AI FrameworkYou’ll integrate RAG into LangChain and LangGraph, so it can become one tool in a larger AI Agent that can decide when to use RAG – and when to follow other tools or workflows. This is how modern, Agentic AI systems are built in practice.
- Fully explained, fully customizableEvery step is explained in detail:
- Multiple ways to load and split DocumentsDifferent Summarization Strategies (Stuff, Map-Reduce, Refine)Several Retrieval Strategies and their trade-offsAlternatives and Options at each step
- Detect Content and Metadata Changes in Documents and FoldersAutomatically Update Embeddings and Vectors in ChromaDBKeep your RAG System in sync with your real document repositories
