(For Product Managers) Understanding the Basics of LLM and Planning LLM-based Services

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

By enabling junior developers to understand the entire development process from planning to code writing, deployment, and verification using artificial intelligence as a development tool, it helps them survive and thrive by acquiring the full-spectrum capabilities needed in the AI era.

This lecture is designed for understanding even if you have no prior knowledge of what Secure Coding (Security Coding) is. After covering basic security coding concepts, it focuses on web service security. This was a special lecture presented under the auspices of OO University. It presents over 160 source codes with rich comments, pinpointing core topics and techniques immediately applicable in practice.

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

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 also be translated as problem-raising or problem-framing. It can also be translated as problem-setting or problem-definition. It is a concept that encompasses the process of questioning known facts such as requests or common sense from new perspectives, defining problems, and working out ways to solve these problems. While problematization should be the starting point of all development, it remains an insufficiently discussed topic in the development field. Carrying out projects or developing programs is, in fact, about creating plans to solve problems. In other words, it is related to problematization. To solve a problem, the problem must first be clearly defined. However, problems are usually given in the form of vague requests. Therefore, having the ability to transform vague requests into clear problems reduces unnecessary 'development waste,' facilitates smooth collaboration, and enables proper understanding of users' real needs. This course helps train 'problem-constructing thinking' 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. 🍽️

Now is the era of development using AI. To create better, more accurate code and documents by better utilizing AI, methods are needed, so we propose suitable ones.

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 methods for creating an AI judge by combining multiple agent frameworks such as CrewAI, LangChain, and AutoGen that enable the implementation of LLM-based multi-agent technology, along with knowledge graphs and reasoning systems.

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.

This explains the foundational principles of large language models like ChatGPT with a focus on theory.

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

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

This comprehensive course teaches you how to build any full-stack application using AI tools like Cursor AI, Claude Code, v0, ChatGPT, and Replit. Learn coding basics, front-end and back-end development, database integration, debugging, and deployment. Perfect for beginners and anyone looking to leverage AI to build real web and mobile apps quickly and efficiently.

Stop studying coding syntax! This is an ultra-fast practical course where you chat with AI (Vibe Coding) to build a RAG chatbot that perfectly understands internal company documents in the Google IDX environment and deploy it live on the web in real-time.

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.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.

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.

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.

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

Get a clear view of exam types and question trends! Strengthen your test-taking skills with 120 total questions. Perfect preparation for the AWS AI Practitioner practice exam with systematic learning, even when studying alone

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).