（企画者のための）LLMの基礎理解

This enables junior developers to understand the entire development process from planning to code writing, deployment, and validation by using artificial intelligence as a development tool, allowing them to survive and thrive in the AI era by acquiring the full-spectrum capabilities needed.

This course is a systematic learning process for prompt engineering, which is a core technology for effectively utilizing large language models (LLMs) or generative artificial intelligence. It covers a wide range of topics from basic 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.

Explains the basic principles of large language models like ChatGPT, focusing on theory.

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.

👨‍🍳 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. 🍽️

Problematization (문제화) is a term also translated as problem posing, problem framing, problem setting, or problem definition. This concept involves questioning known facts (like requests or common sense) from a new perspective, defining the problem, and devising methods to solve it. Problematization should be the starting point for all development, yet it remains insufficiently discussed within the field. Performing projects or developing programs is, in essence, planning to solve problems. This means they are related to problematization. To solve a problem, it must first be clearly defined. However, problems are mostly presented as vague requests. Therefore, the ability to transform vague requests into clear problems is essential to reduce unnecessary 'development waste,' facilitate smooth collaboration, and accurately grasp users' true needs. This lecture helps train 'the thinking of problem construction' through practical examples 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' necessary when planning applications or web services based on LLMs, 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."

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.

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

Describes in detail the various language models developed from the inception of Natural Language Processing technology to the process leading up to the latest LLM models.

We create an AI judge by combining CrewAI, facilitating LLM-based multi-agent technology, with knowledge graphs and inference 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.

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

Describes in detail the various language models developed from the inception of Natural Language Processing technology to the process leading up to the latest LLM models.

Natural Language Processing (NLP) is one of the fastest growing areas of artificial intelligence. This course covers a wide range of topics, from the basics of NLP to the latest NLP techniques using deep learning. In particular, it provides an in-depth understanding of cutting-edge generative models such as ChatGPT.

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.

"An introductory hands-on course for creating domain-specific customized LLMs with LoRA-based lightweight fine-tuning!" You can now create customized LLMs yourself. This course is designed to help you easily learn the process of creating domain-specific LLMs through LoRA-based lightweight fine-tuning practice, without complex theory. Even those handling LLMs for the first time can follow along, as we guide you step-by-step through the entire process: loading models → inputting data → training → comparing results. Learn the latest techniques for creating LLMs specialized in specific fields with minimal resources through direct hands-on practice!

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

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.

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.