Retrieval Augmented Generation (RAG)

Large language models are powerful, but without access to the right information, they often make mistakes. RAG fixes that by grounding model responses in relevant, often private or up-to-date data. As LLMs move into real products and workflows, the ability to build robust, reliable RAG systems is becoming a must-have skill for engineers working in AI.

This course, taught by AI engineer and educator Zain Hasan, gives you the hands-on experience and conceptual understanding to design, build, and evaluate production-ready RAG systems.

You’ll learn to choose the right architecture for your use case, work with vector databases like Weaviate, experiment with prompt and retrieval strategies, and monitor performance using tools like Phoenix from Arize.

Throughout the course, you’ll build progressively more advanced components of a RAG system, using real-world datasets from domains like e-commerce, media, and healthcare. You’ll also explore critical tradeoffs, like when to use hybrid retrieval, how to manage context window limits, and how to balance latency and cost, preparing you to make informed engineering decisions in practice.

RAG is at the core of LLM systems that need to be accurate, grounded, and adaptable, whether for internal tools, customer-facing assistants, or specialized applications. This course helps you move beyond proof-of-concept demos into real-world deployment, equipping you with the skills to build, evaluate, and evolve RAG systems as the ecosystem grows.

Start building RAG systems designed for real-world use.

You’ll earn a certificate upon completing the course, recognizing your skills in building and evaluating RAG systems with real-world tools and techniques

Intermediate Python skills required; basic knowledge of generative AI and high school–level math is helpful.