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."

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 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.