LangChain & LangGraph Mastery : RAG, Agents & AI Workflows

About this course

This comprehensive course is designed to take you from the fundamentals of LangChain to advanced, production-ready AI workflows using LangGraph. It is a complete, end-to-end guide for developers, data scientists, and AI engineers who want to build real-world applications powered by Large Language Models (LLMs).

Unlike surface-level tutorials, this course focuses on how modern LLM systems are actually built in practice — from prompt design and structured outputs to embeddings, vector databases, retrievers, full RAG pipelines, and finally workflow orchestration using LangGraph.

You will not only understand how each LangChain component works individually, but also how they connect together to form scalable, maintainable, and production-ready AI systems.

What Makes This Course Different

This course is built with a code-first, system-level approach. Every concept is explained in detail and then implemented step by step using real examples. By the end of the course, you will have a clear mental model of how LLM applications are architected in the real world.

You will learn how to:

• Control LLM behavior using structured prompting and output parsing
• Build reliable pipelines instead of fragile prompt hacks
• Design Retrieval-Augmented Generation (RAG) systems that actually scale
• Move from linear chains to graph-based workflows using LangGraphLangChain Fundamentals & LLM Integration

You begin with a strong foundation:

• What LangChain is, why it exists, and how it fits into the modern Generative AI ecosystem
• Python setup and LangChain installation
• Integration with popular LLM providers such as OpenAI, Hugging Face, Anthropic, Gemini, and others
• Understanding core concepts like Chains, Agents, Memory, and Tools
• This section ensures that even beginners can confidently follow the rest of th

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