LangChain & LangGraph Masterclass: Build Production AI App

About this course

Build Production-Ready AI Applications for Real-World Software Development

Most AI courses teach prompts, simple chatbots, or isolated code examples. Real-world AI applications require much more than calling an LLM API.In this course, you'll learn how to design, build, test, secure, monitor, and deploy production-ready AI applications using the modern LangChain ecosystem and industry best practices.

Starting with the fundamentals of Large Language Models (LLMs), prompt engineering, and AI application architecture, you'll progressively build real-world projects using LangChain, LangGraph, LangSmith, FastAPI, vector databases, Docker, and modern AI engineering techniques.

By the end of this course, you'll understand not only how AI applications work, but also how professional engineering teams build systems that are scalable, reliable, secure, and ready for production.

What You'll Learn

You'll learn how to:

• Build Retrieval-Augmented Generation (RAG) applications
• Create stateful AI agents with LangGraph
• Develop production-ready AI APIs using FastAPIAdd observability and tracing with LangSmithIntegrate vector databases for semantic search
• Build streaming AI applications
• Secure AI services using JWT authentication
• Deploy containerized AI applications with DockerTest and validate AI systems using modern AI quality engineering practices
• Design maintainable, scalable AI architectures for real-world deployment

Hands-On Projects

Throughout the course, you'll build practical projects including:

• Production-grade RAG applications
• LangGraph orchestration workflows
• Multi-step AI pipelines
• FastAPI AI backends
• Streaming AI applicationsJWT-secured A

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