Retrieval Augmented Generation (RAG) Course
Build Retrieval Augmented Generation (RAG) systems for real-world applications. Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation.
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RAG for real-world applications: Learn how retrieval and generation work together, and how to design each component to build reliable, flexible RAG systems.
Search techniques and vector databases: Use techniques like keyword search, semantic search, hybrid search, chunking, and query parsing to support RAG applications across domains like healthcare and e-commerce.
Prompt design, evaluation, and deployment: Craft prompts that make the most of retrieved context, evaluate RAG system performance, and prepare your pipeline for production.
The benefits of RAG
RAG helps large language models generate more accurate and useful responses by retrieving relevant information from knowledge bases of information they weren’t trained on. These sources of information are often private, recent, or domain-specific, which gives an LLM more context to provide grounded answers. In this course, you’ll learn how to design and implement every part of a RAG system, from retrievers and vector databases to large language models, and evaluation platforms. You’ll understand fundamental principles and apply key techniques at both the component and system levels to effectively connect LLMs to relevant external data sources.
Why Enroll?
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
Instructor
Zain Hasan
Zain Hasan is an AI engineer and educator with nearly a decade of experience building and teaching machine learning systems. He combines deep technical knowledge with a practical approach to system design, helping you understand both the theory and the real-world tradeoffs behind RAG architectures. His teaching style feels less like a lecture and more like learning from a sharp, thoughtful teammate who’s been there before.
- 5 Modules
- Self-paced
- Intermediate
Learn through real-world projects
Across five modules, you’ll complete hands-on programming assignments that guide you through building each core part of a RAG system, from simple prototypes to production-ready components.
Through hands-on labs, you’ll:
Build your first RAG system by writing retrieval and prompt augmentation functions and passing structured input into an LLM.
Implement and compare retrieval methods like semantic search, BM25, and Reciprocal Rank Fusion to see how each impacts LLM responses.
Scale your RAG system using Weaviate and a real news dataset—chunking, indexing, and retrieving documents with a vector database.
Develop a domain-specific chatbot for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset.
Improve chatbot reliability by handling real-world challenges like dynamic pricing and logging user interactions for monitoring and debugging.
Develop a domain-specific chatbot using open-source LLMs hosted by Together AI for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset.
Course Syllabus
Recommended Background
Intermediate Python skills required; basic knowledge of generative AI and high school–level math is helpful.
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