(For Product Managers) Fundamentals of LLM and Understanding LLM-based Service Planning

[2nd edition scheduled for completion in May 2026] You can quickly understand the basic concepts and syntax of the C language.

[The 2nd edition is scheduled for completion in 2026] You can develop your programming skills while exploring the fundamental principles of Large Language Models (LLMs) such as ChatGPT and Claude in depth. You can learn a variety of information and development techniques that are often difficult to come by.

[2nd Edition scheduled for completion in 2026] Covering everything from prompt engineering theory to practical techniques, latest application cases, and security/ethical issues, this provides practical help to LLM-based service developers, data scientists, and AI planners alike.

[Scheduled for completion of the 2nd edition in April 2026] Penetrating the essence of basic statistics necessary for AI development and application without a single formula or line of code.

[The 2nd edition is scheduled for completion in April 2026] This helps junior developers understand the entire development process, from planning to code writing, deployment, and verification, by using artificial intelligence as a development tool.

[Scheduled for completion of the 2nd edition in May 2026] Helps train the "structured thinking" required to solve problems.

[Scheduled for completion of the 2nd edition in 2026] We present ways to improve RAG performance based on Cognitive Load Theory. To overcome the limitations of existing RAG performance enhancement techniques, we synthesize various methods by applying the conceptual framework of cognitive load.

[Scheduled for completion of the 2nd edition in 2026] You can learn the 'service design' concepts and methods required when planning applications or web services based on LLM, a type of Generative Artificial Intelligence (Generative AI).

[Scheduled for completion of the 2nd edition in 2026] "I only changed one name, but the code became different." We have organized the core of readable code, 'naming that reads well,' from principles to examples. Easy-to-read names are the beginning of collaboration.

[2nd Edition scheduled for completion in 2026] This course is designed to be understood even if you have no prior knowledge of Secure Coding. It covers basic secure coding concepts first, followed by a focus on web service security. This lecture was originally conducted as a special seminar hosted by OO University. With over 160 source codes provided alongside rich commentary, it pinpoints core topics and techniques that can be applied immediately in practical work.

[The 2nd edition is scheduled for completion in 2026] Presents various clean coding techniques.

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

[Scheduled for completion of the 2nd edition in 2026] This book provides detailed explanations of various language models developed throughout the process, starting from the beginnings of natural language processing technology to the latest LLM models.

[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.

[2nd Edition scheduled for completion in 2026] We will look at how to collect and analyze user requirements for planning LLM services.

[Scheduled for completion of the 2nd edition in 2026] Explores the limitations of LLMs, the latest technologies to overcome them, and projected future technologies.

[Scheduled for completion of the 2nd edition in 2026] We will explore the types of applications and web services that can be built using LLMs.

[Scheduled for completion of the 2nd edition in 2026] This book provides detailed explanations of various language models developed throughout the process, starting from the beginnings of natural language processing technology to the latest LLM models.

[2nd Edition scheduled for completion in 2026] We will look at how to collect and analyze user requirements for planning LLM services.

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

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

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

Learn the core techniques of 'Prompt Engineering' to get exactly the answers you want from AI. From a complete beginner's first steps to expert practical applications, maximize your AI utilization skills!

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

The era of Physical AI, as mentioned by Jensen Huang in 2025, is coming—alongside Perception AI, Generative AI, and Agentic AI. Experience Physical AI firsthand, quickly and easily. Explore Physical AI using Hugging Face's LeRobot library and actual physical robots.

"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!

The Master Guide to AI for Java Developers! This course shatters the prejudice that AI is the exclusive domain of Python and empowers your Java applications with AI-driven eyes, ears, voice, and even coding capabilities. Grow into an AI Architect based on the OpenAI API. You will directly develop various AI experts such as an AI writer, translator, artist, and coding companion. You will also build a multi-functional AI assistant that integrates core features including text summarization, sentiment analysis, image description, Java code generation, speech-to-text/text-to-speech conversion, and image/subtitle generation. This is a golden opportunity to maximize the competitiveness of existing Java systems by learning how to assign AI roles through system messages and even implement harmful content moderation.

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

This course covers the fundamentals of the Notion API and teaches you how to automate Notion by integrating AI (Claude) using Notion MCP. You'll learn to directly control blocks, pages, and databases by following the official API documentation, and ultimately complete a hands-on project that creates databases and automatically adds pages by analyzing text file content using only natural language commands (prompts).