LangChain Framework for Beginners – Build AI Systems + RAG

If you’ve been hearing words like “AI Agents”, “LangChain”, or “RAG” everywhere and wondering how people are building smart AI apps that can search data, answer questions, or take actions on their own — you’re in the right place. This beginner-friendly course will show you how to build real AI applications from scratch, even if you have never worked with AI before. Most people only use ChatGPT to write text… But companies today are building applications that can think, read, search, decide, and act automatically. These applications are powered by LangChain — the leading framework for building AI-powered software. In this course, you will learn how to: - Build simple AI apps that actually do useful tasks - Make an AI use tools (like checking the weather or sending an email) - Let an AI read documents and give answers based on your data - Organize information so the AI can “remember” things - Build small automation systems that can save time and effort We will start from zero, explain every concept in plain English, and build everything step-by-step together. By the end of the course, you will be able to build your own AI apps that can be used for: - Personal projects - Business automation - Customer support - Data lookup - Testing and QA - Real-world workflows - No complex math - No advanced AI background - No prior LangChain knowledge required. Just practical learning, short lessons, real projects, and the confidence to build your own AI applications. If you want simple, hands-on learning that focuses on doing — not theory — this course is perfect for you.

What you'll learn:

• Build AI Agents with LangChain using tools, memory, prompts, and multi-model configurations.
• Create custom tools with Zod validation and enable LLMs to choose and execute tools intelligently.
• Implement dynamic system prompts, middleware, and context injection for accurate agent responses.
• Design complete RAG pipelines using embeddings, vector stores, similarity search, and document loaders.
• Integrate LangChain Agents with external APIs, databases, and MCP servers for real-world automation.
• Deploy production-ready LangGraph agent servers with environment setup, tracing, and safety guardrails.