인프런 - 라이프타임 커리어 플랫폼

Building a Retrieval-Augmented Generation (RAG) Intelligent AI Agent with LangGraph! From theory to practice, this is a hands-on tutorial that even beginners can easily follow.

LangGraph, packed with a major corporation's AI Agent lead's know-how. We deliver knowledge gained from real-world challenges.

AI Agent: A total integration of the latest AI technology! Implement various AI agents and learn to build your own AI agent using LangGraph.

"Mastering Model Context Protocol (MCP)" is a practice-oriented engineering course designed to help developers build secure, production-ready AI backends. Based on my experience helping thousands of students resolve confusion in LLM integration, Tool Calling, and backend architecture, I aimed to address the most common challenges through this course. "How can I build a reliable backend that an LLM can call securely?" "Should I choose SSE, stdio, or streamable-http?" "How can I scale MCP to a real-world application level using FastAPI, Auth0, and LangGraph?" "How should I structure MCP Tools, Resources, Prompts, and Context?" This course provides detailed, step-by-step guidance, from running a minimal MCP server to deploying a fully secured, Docker-based system. All lessons are practice-focused, centering on reducing complexity and providing clear, repeatable workflows for building modern AI systems. If you are tired of vague tutorials and want a clear, concrete, engineering-level understanding of MCP, this course was made for you.

Are you anxious every time you deploy an AI agent? Based on experience with major domestic corporations and global big tech companies, we will show you how to systematically measure and improve agent quality using LangSmith.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

No more AI toy projects that end with a single API call! Based on my experience designing and deploying a full-stack AI chatbot, I will guide you through the entire process of building an AI chatbot that "works as a real service"—covering streaming, tool calling, memory, multi-modal capabilities, and Human-in-the-Loop.

The era of AI simply providing answers is over. Now is the age of LLM Agents that make decisions and take actions on their own. This course is an introductory lecture where you learn the core principles and structure of agents by implementing them yourself in just 90 minutes of hands-on practice. With minimal complex theory and a code-focused practical flow, you can directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

Learn how to utilize various local LLMs (Qwen, Gemma) and explore different techniques to efficiently evaluate and improve the performance of LLM systems.

This is a course that beginners can follow along with in a fun and easy way. Beyond simple chatbots, AI Agents that automate industry-specific business workflows. This course quickly covers the basic structure and core technologies of AI Agents (LangChain, LangGraph, RAG) in 1.5 hours, and is an introductory course where you build practical skills by directly creating mini agents that work with actual code. After completing the course, you'll understand the necessity of industry-specific data preprocessing and scalable design, and be prepared for advanced concepts covered in deeper courses.