It explains in detail the various language models developed in the process, starting from the beginnings of natural language processing technology to the latest LLM models.

Explore the limitations of LLMs, the latest technologies to overcome them, and projected future technologies.

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.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

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

Master the complete lifecycle of building modern Generative AI applications using Large Language Models, Retrieval-Augmented Generation, and Agentic AI systems. Learn to design enterprise-grade AI solutions from prompt engineering to deployment, combining LLMs with vector databases and external knowledge sources for production-ready applications.

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

Learn the entire process of running the latest Gemma 4 model directly on your MacBook without the burden of paid API costs. You will master performance maximization techniques using Apple Silicon's Metal API and optimal parameter settings for different VRAM capacities, while gaining the ability to build production-level local AI infrastructure based on FastAPI.

Using AI has become easier, but model cards and papers are still difficult to read, even just a few lines. Although you can look up the meanings of individual words, it’s not clear how those words connect to one another. This course builds up around 160 essential terms that frequently appear in AI documents, in the easiest order to understand them, so that in each lesson you can accurately read a single sentence from an actual document.

Have you wanted to implement RAG but felt overwhelmed by connecting everything from embeddings to vector storage and search? Based on real-world service development experience, this course explains the core concepts of pgvector and the service source code. By calling the API directly, you can quickly understand the entire flow of how RAG vector search works.

This course uses Python, a programming language well suited for big data analysis, to directly collect, process, and analyze social big data from the web, social media, and other sources. Python programming exercises are conducted in a step-by-step, follow-along format, making it easy for humanities and social science researchers with no programming experience to learn. By completing this course, you will be able to master Python, collect data, and analyze big data.

We design and implement AI agent systems that go beyond simple chatbots to perform tasks autonomously. From Sense-Think-Act loop-based architectures to LangGraph cyclic structures, RAGAS and LangSmith-based quantitative evaluation, and operational guardrails, you will complete your enterprise-grade agent engineering capabilities through hands-on practice of the entire process.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

This is a practical course on building a RAG-based document search system using the OpenAI API and Spring AI in a Spring Boot environment. You will learn the entire process of developing an AI assistant step by step, from the concepts of Prompt Engineering, Vector Stores, and Embeddings to rapid prototyping with Dify, MCP tool integration, multimodality, and guardrails. You can learn practical application strategies by comparing direct implementation with the use of no-code tools. 🆕 [Update] Section 4 now includes a supplementary lecture (1 hour) on TypeSafe’s Jev decision-making model, covering how to design an agent’s decision-making logic in greater depth.

Go beyond a simple system prompt to build an AI character chatbot that remembers users and whose relationship with them evolves, using persona design and Structured Outputs. In this 30-minute hands-on session, you’ll learn how to safely call the OpenAI API based on the provided starter project, process emotions, trust levels, and memories in a verifiable format using JSON Schema and Zod, and use an AI coding agent within defined constraints.

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!

You can learn everything from basic usage of generative AI to automation and creating customized AI chatbots for practical work applications all in one comprehensive course.

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

Explore how LLM applications are shifting the development paradigm. Understand why LLMs are becoming a sustainable core technology through comparisons with existing technologies, and learn about the essential skills and mindset shifts required for developers. Gain insights from expert instructors to grasp the essence and future direction of LLM application development.