Moving beyond prompt engineering and context engineering, we are now entering the era of harness engineering. Harnesses can solve LLM-based challenges that are difficult to achieve with prompts and RAG alone.

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

It enables junior developers to understand the entire development process, from planning to coding, deployment, and verification, by using AI as a development tool.

It covers a wide range of topics, from prompt engineering theory and practical techniques to the latest application cases and security/ethical issues, providing practical assistance to LLM-based service developers, data scientists, and AI planners alike.

It helps train the "constructive thinking" needed to frame problems.

We present ways to improve RAG performance based on Cognitive Load Theory. To overcome the limitations of existing RAG enhancement techniques, we synthesize various methods by applying the conceptual framework of cognitive load.

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).

"I only changed a name, but the code has transformed." I have summarized the core of readable code, 'naming that reads well,' from principles to examples. Easy-to-read names are the beginning of collaboration.

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.

You can quickly understand the basic concepts and fundamental syntax of the C language.

We present various clean coding techniques.

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

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 look at how to collect and analyze user requirements for planning an LLM service.

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

We will explore the types of applications and web services that can be built using LLMs.

Explains the reasons why LLM is necessary, the technical background, and basic concepts for LLM planners and LLM application planning managers.

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

Explains the reasons why LLM is necessary, the technical background, and basic concepts for LLM planners and LLM application planning managers.

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

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

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.

"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 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!

"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.

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.

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.

This is a course covering the GitHub notebooks and bonus content from <Build a Large Language Model from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Build a Large Language Model from Scratch> is the Korean translation of the bestseller <Build a Large Language Model (from Scratch)> (Manning, 2024) by Sebastian Raschka. This book provides a way to learn and utilize the operating principles of large language models by building a complete model starting from scratch with OpenAI's GPT-2 model.