Practical Retrieval-Augmented Generation gives you a hands-on, end-to-end understanding of how to upgrade large language models by connecting them to external knowledge sources. Instead of relying solely on what an LLM learned during training, you’ll learn to extend its intelligence with real-time, domain-specific information using Retrieval-Augmented Generation (RAG). By the end of this course, you’ll be able to design, build, evaluate, and deploy a complete RAG system—from embeddings and vector databases to evaluation frameworks and advanced enhancements like GraphRAG.
 
What You’ll Learn
Understand LLM types and how different model families (auto-encoders, auto-regressors, hybrids) fit into RAG architectures.
Build a full RAG application using vector databases and multiple embedding models.
Experiment with top generators such as GPT-4o, Claude, Command-R, and other open/closed-source models.
Create your own RAG API for scalable integration into applications.
Develop a chat interface powered by your custom retrieval pipeline.
Explore advanced methods, including re-ranking strategies and GraphRAG knowledge-graph retrieval for higher accuracy.
 
Who Should Take This Course
Ideal for:
Developers, data scientists, ML engineers, and AI practitioners looking to enhance LLM accuracy and reduce hallucinations through retrieval.
Anyone building production AI systems that rely on up-to-date, factual, or domain-specific information.
 
Course Requirements
Proficiency in Python 3 and experience with interactive environments (Jupyter, Colab, Kaggle).
Familiarity with Pandas for data manipulation.
Solid understanding of ML fundamentals: train/test splits, loss functions, gradient descent.
 
Lesson Breakdown
 
Lesson 1 — Introduction to Retrieval-Augmented Generation
Learn the building blocks of a RAG system: retrievers, embeddings, vector stores, and generators—and how they come together to deliver dynamic, context-aware outputs.
 
Lesson 2 — Building the Foundations
Understand the landscape of LLMs, their architectures, and their roles in retrieval systems so you can choose the right model for the right task.
 
Lesson 3 — Advanced Prompt Engineering Techniques
Master the structure of effective prompts and how LLMs interpret tasks. Learn how to improve reliability, consistency, and accuracy using prompt patterns and iteration.
 
Lesson 4 — Developing a RAG System
Assemble your first functional RAG pipeline and explore a range of embedders and generators—open-source and proprietary. Test a full chatbot workflow end-to-end.
 
Lesson 5 — Evaluating and Testing RAG Systems
Move from intuition to measurable performance. Evaluate retriever precision/recall and judge generator quality, safety, and consistency with real metrics.
 
Lesson 6 — Expanding and Applying RAG Systems
Push beyond the basics: fine-tune embedders, apply rerankers, and integrate graph-based retrieval to upgrade your system into a next-generation GraphRAG application.