Prompt Patterns for Developers

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

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.

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

Explains why LLMs are needed, their technical background, and basic concepts.

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.

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. 🍽️

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.

Stop copy-pasting code! 🙅‍♂️ This is a "real" conceptual lecture that dives deep into everything from how agents work to their architecture and design philosophy. Beyond just learning how to build them, I will help you develop an eye for designing entire systems. Build a solid foundation here that remains unshakable amidst the technology trends of 2026! 🧠✨

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

Learn AI utilization methods and performance improvement strategies tailored to real-world business situations. (i.e., you won't learn things like automatic short-form video generation.) This is a practical course where you learn to leverage AI not merely as a tool, but as an ultimate instrument for business efficiency. By utilizing various generative AI and the latest AI agents such as ChatGPT, Gemini, NotebookLM, Gesnpark, Skywork, etc., you will master AI prompt utilization → AI data analysis → and AI task automation implementation, all at once. This class is optimized for planners, marketers, managers, founders, and others who wish to acquire business productivity and performance enhancement strategies directly applicable to their work.

Apply the latest trends of Generative AI directly to your work! The bible of Prompt Engineering lectures, packed with essentials

A basic LangChain course that extracts only the essentials from the official documentation, all for the price of a cup of coffee(?). Get familiar with LangChain syntax before diving into full-scale development!

To enable office workers to actively and effectively use AI in their work, we provide various techniques for reducing 'Hallucination' (i.e., nonsensical) answers and accurately eliciting desired responses.

“My idea can make money? Is AI monetization really possible?” Are you frustrated with the same paycheck every month? Do you have brilliant ideas in your head but feel lost on how to actually get started? Everyone says ‘AI’ is the trend, but does it sound like a distant story for someone who isn't tech-savvy? What if you could turn your ideas into ‘selling digital products’ and even create a system that generates income while you sleep—even if you don't know a single line of code or have any marketing experience? This course is a practical guide that teaches you exactly how to do that.

ChatGPT: How Far? Series, Stock auto-trading app & web inv. report creation!

This course goes beyond simple trends, covering how to systematically learn to collaborate with AI tools, verify and improve output quality, and build and deploy applications from start to finish. Anyone can take this course, from developers interested in AI to team leaders looking to boost team productivity.

If you've ever thought... "I wish I had JARVIS, Iron Man's perfect assistant"?! Complete everyone's JARVIS to help with your smart work management!

Throw away those simple services you can make with vibe coding in just 10 minutes! You can learn 'real' practical vibe coding. Let's create advanced services at the level of actual services according to planning and design with Cursor AI NCOP. No Code, Only Prompt

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!

Depending on how you write the prompt, AI's capabilities can expand infinitely. The benefits gained from collaboration with AI depend on the clarity of the questions and the provision of prior information. Now, try to effectively utilize GPTs to perform efficient and purpose-driven tasks.

A quick, essential lecture for mastering ChatGPT! Let's learn fast!

I will tell you the "formula" for creating 챗GPT prompts. If you apply only this formula, good prompts will pour out.

We will summarize the core theories spanning from Machine Learning and Deep Learning to Generative AI in one go. And by implementing various Generative AI applications with easy Python code, you can take your first step as an AI developer.

This practical introductory course is designed to help office workers utilize generative AI not just as a simple tool, but as a strategic business partner. The objective is to develop the ability to use AI safely while complying with security and copyright standards, based on a precise understanding of its operating principles and limitations. Participants will learn step-by-step—from the core principles of prompt engineering to streamlining text-based tasks such as planning, reporting, emailing, and drafting meeting minutes, as well as Excel data analysis and the utilization of images and PDF documents. This course helps drastically reduce time spent on repetitive tasks and empowers individuals to handle multiple roles effectively. Going beyond personal use, the curriculum covers how to build team-level work templates and standards. This practice-oriented program is structured to ensure that upon completion, participants possess immediate, applicable workflow automation assets that lead to increased productivity for the entire organization.

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 introduces core principles and practical uses of various generative AI technologies (text, images, video, music), helping foster creative problem-solving skills.

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.