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

It explains the fundamental principles of large language models like ChatGPT, focusing on theory.

This lecture is designed to be easily understood even if you have no prior knowledge of Secure Coding. After covering fundamental secure coding concepts, it focuses on web service security. This course was originally conducted as a special lecture hosted by OO University. It provides over 160 source codes with extensive comments, pinpointing core topics and techniques that can be applied immediately in the field.

Without a single formula or line of code, this penetrates the essence of basic statistics necessary for AI development and application.

By enabling junior developers to use AI as a development tool to understand the entire development process—from planning and coding to deployment and verification—we empower them to survive and thrive in the AI era with the full-spectrum capabilities required.

This course systematically teaches prompt engineering, a core technology for effectively utilizing Large Language Models (LLMs) and generative artificial intelligence. It comprehensively covers everything from foundational theory to practical techniques, as well as the latest application cases and security/ethical issues, providing practical help to LLM-based service developers, data scientists, and AI planners alike.

Problematization is a term that can be translated as "raising a problem" or "problem framing." It can also be interpreted as problem setting or problem definition. It is a concept that involves questioning known facts—such as requirements or common sense—from a new perspective, defining the problem, and structuring the process to solve it. While problematization should be the starting point for all development, it is a topic that has not yet been sufficiently discussed in the field. Carrying out a project or developing a program is, in fact, the act of creating a plan to solve a problem. In other words, it is inherently linked to problematization. To solve a problem, the problem must first be clearly defined. However, most problems are given in the form of vague requests. Therefore, having the power to transform vague requests into clear problems can reduce unnecessary "development waste," facilitate smooth collaboration, and accurately identify the true needs of users. This course helps you train your "constructive thinking" regarding problems through practical cases and tools.

What should be done when building a generative AI or LLM-based RAG (Retrieval-Augmented Generation) system, but the desired performance isn't achieved and there's no suitable solution? This lecture presents methods to improve RAG performance based on Cognitive Load theory. Through this lecture, you will understand the limitations of LLM context windows and learn how to effectively manage cognitive load in RAG systems. It is a practical-level theoretical lecture covering Chunk size and structure design, high-quality Chunk generation techniques, dynamic optimization, performance evaluation, and practical techniques.

You can learn the concepts and methods of 'service design' needed when planning applications or web services built on LLM, a type of generative artificial intelligence (generative AI).

"One name change, code transformed." "The key to readable code, 'naming for readability,' summarized from principles to examples." "Easy-to-read names begin collaboration."

You can quickly understand C language's basic concepts and grammar.

👨‍🍳 Like cooking code, neatly and deliciously! 『Clean Coding』 is a code cooking class for chef-like developers, where they joyfully learn clean coding through a cooking analogy. 🍽️

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

Starting from the origins of natural language processing technology, this provides a detailed explanation of the various language models developed in the journey leading up to the latest LLM models.

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.

We will examine methods for collecting and analyzing user requirements to plan LLM services.

We explore the limitations of LLMs, methods to overcome them, and the latest research topics.

Let's explore the types of applications or web services that can be built using LLMs.

Starting from the origins of natural language processing technology, this provides a detailed explanation of the various language models developed in the journey leading up to the latest LLM models.

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

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 is a course to master the fundamental theories of LLM and the core technologies of LangChain and RAG. You can easily learn the latest AI technologies used in practice, starting from LLM basics!

We are now in an era where anyone can create services without coding. The Selfish Club crew members implemented services for actual use in just three weeks by selecting only the essential features. These are examples of crew members who don't know how to code, solving problems themselves by building services using only natural language prompts. Vibe coding is no longer a foreign concept! We are sharing all the real-world examples of "what problems can actually be solved through coding!"

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.

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 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.

"Beyond Data Analysis: Mastering Predictive Modeling with 5 Real-World Projects (45 Lectures Total)" Have you finished learning data analysis but feel stuck when it's time to actually build a model? Beyond simply learning how to call libraries, this course will help you fully master the inner workings of algorithms and optimal model validation strategies—ranging from Titanic survival prediction to spam text classification. Systematically conquer projects that wield the most power in the industry, from linear models to the latest ensemble algorithms and the basics of Natural Language Processing (NLP). Step into the world of AI modeling and start predicting the future based on analyzed data.

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.

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.

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

"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!

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).