LangChain Fundamentals in One Hour

RAG. Learn from Silicon Valley GenAI Hackathon Winner. Packed with real-world know-how.

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.

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

How to Use Storybooks A to Z. We show you everything about storybooks.

Test code! For those who are at a loss as to where to start, here it is. From writing tests to deploying through automation, all in one place!

New to Flutter? A Flutter Contributor will guide you step-by-step! Start Flutter dev with an AI project using Gemini.

This is for those who want to deploy/operate services with AWS. From domain settings to Docker and serverless!

This course is a practice-oriented lecture that goes beyond 👉 just explaining the concepts of RAG (Retrieval-Augmented Generation), 👉 to building the actual working architecture yourself, 👉 and experiencing expansion and optimization. Starting from simple RAG examples, you will learn step-by-step—from Advanced RAG to Modular RAG and Agent-based RAG—at a level that can be immediately applied in the field.

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

LLM: A pro quickly delivers core content, from essentials to practical tips!

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

[2nd edition scheduled for completion in 2026] We explore how to create an AI judge by combining various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology, along with knowledge graphs and reasoning systems.

This course covers the entire process of designing and implementing Generative AI services through step-by-step hands-on practice, focusing on LangChain 1.0, LangGraph, and Deep Agents. Starting from the basics of Chat Models and Messages, you will master the core building blocks of LangChain—including Tool Calling-based Agents, memory, streaming, and structured output (Pydantic-based)—and then expand into LangGraph's StateGraph-based state machine architecture to directly implement production-ready AI system structures. You will systematically build agent design capabilities through real-world scenario-based exercises, such as RAG systems based on documents, PDFs, and web data (embeddings, ChromaDB, semantic search), SQL Agents (Chinook DB), multi-agent orchestration using the Supervisor pattern, and calculator agents utilizing the LangGraph Graph API. Furthermore, through Deep Agents (create_deep_agent), you will complete Generative AI applications equipped with stability, scalability, and controllability by utilizing sub-agent delegation, multi-turn conversations, and Deep Agents-specific middleware (SummarizationMiddleware, HumanInTheLoopMiddleware, ToolRetryMiddleware, PIIMiddleware, etc.). 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph/Deep Agents 👉 For those who want to implement RAG and Agents as production-level architectures rather than just "demos" 👉 This is the optimal course for those who need a practical roadmap covering state-based agents, SQL/document automation, and multi-agent orchestration.

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

This is the edited live video from the LLM Application Experience Sharing Session with knowledge sharer Kang Byung-jin :)

With v0, cursor AI, and various LLMs, from PRD to AI product recommendations! Complete your Next.js shopping mall through a practical project.

Throw away those simple services you can make with vibe coding in just 10 minutes! You can learn 'real' practical vibe coding. Let's create advanced services at the level of actual services according to planning and design with Cursor AI NCOP. No Code, Only Prompt

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

By simply entering a US company ticker, this system automatically collects financial accounting data, competitor analysis, recent news, and stock price information to generate professional investment reports through in-depth analysis using the OpenAI GPT API. The final results are automatically uploaded to Notion. By learning the basic concepts and terminology of financial accounting, even engineers can utilize this for competitor benchmarking, persuading supervisors, and planning new solutions. You will build a high-performance API server using Python and FastAPI, learning asynchronous programming, AI integration, and practical fintech skills. We support beginners so they can easily follow along, complete a system applicable to real-world investment and business, and grow into developers tailored for the AI era.

We introduce basic prompt patterns for coding and advanced API prompt patterns for leveraging artificial intelligence.

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).

This course goes beyond simple trends, covering how to systematically learn to collaborate with AI tools, verify and improve output quality, and build and deploy applications from start to finish. Anyone can take this course, from developers interested in AI to team leaders looking to boost team productivity.

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is currently the technology that has come closest to AGI. In this lecture, we will explore LLM and Agent technologies from a layperson's perspective.