Become an AI Engineer: RAG Systems, GenAI & LangChain

About this course

Are you ready to build AI systems that deliver real-world value?

Whether you're a data engineer transitioning into AI engineering, an ML engineer focusing on production systems, or a software architect designing intelligent applications, this course equips you with the skills to build enterprise-grade RAG systems using LangChain, Python, and leading large language models.

You will gain a clear understanding of what retrieval augmented generation (RAG) is, how RAG works, and why RAG is important in modern AI automation. The course moves beyond theory to provide a practical approach to retrieval augmented generation systems, focusing on real-world deployment and scalability.

In today’s enterprise landscape, most data remains unstructured and underutilized. Organizations are investing heavily in retrieval augmented generation to unlock this value—but success depends on strong engineering foundations. This course bridges that gap by teaching you how to design and implement production-ready RAG architecture.

You will learn how to build a complete RAG pipeline, from data ingestion and vector database optimization to advanced retrieval strategies and system integration. Using the LangChain RAG framework, you will implement intelligent workflows, including LangChain agents and agentic RAG patterns for building context-aware AI applications.

The course also addresses key practical questions, including:

• What is a RAG system in AI?
• How does retrieval augmented generation work in production?

What is RAG in GenAI and LangChain?

• How to build and evaluate scalable RAG systems?

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