Prompt Patterns for Developers

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.

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.

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.

It explains in detail the various language models developed in the process, starting from the beginnings of natural language processing technology 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 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.

This is a practical OpenAI Codex course for both non-developers and developers. You will easily learn everything from basic usage to building websites and programs, task automation, harness engineering, and advanced features, while developing the ability to master new features on your own using the latest official documentation.

From AWS EC2 backend operations to automatic error fixing, we are revealing the entire development process of Sidabari (https://github.com/cx8537/sidabari), a Claude Code-based automation tool. In this course, we provide all the actual prompts and conversation history used as learning materials, allowing you to experience the entire prompt input process involved in its creation.

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!

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! 🧠✨

This is a process of developing a practical web service in collaboration with AI agents based on TARS prompt engineering principles. You will gain full-stack development experience covering everything from UI/UX component creation to CRUD functions, API integration, and data visualization, ultimately completing your own idea as a viable MVP.

Using ChatGPT is completely different from developing an actual generative AI service. This is because you must master everything from API calls to conversation state management, structured outputs, function calling, document-based RAG, MCP integration, and AI agent implementation. Based on extensive experience in data analysis and AI instruction, this course explains complex concepts through Python-centered practice. You will learn step-by-step—from the principles of generative AI to the OpenAI Responses API, multimodal features, File Search-based RAG, external tool integration, and the Agents SDK—to complete a fully functional AI application.

You will experience the process of building an actual web service by combining various AI models such as Kimi, OpenCode, and Codex according to the situation. Beyond simple prompt input, you will learn practical development workflows for collaborating with AI, from requirement definition to code review and service completion, through hands-on practice.

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

AI literacy refers to the "ability for general users, not just technicians, to effectively utilize AI in their practical work and daily lives," as well as the "competency to critically evaluate AI." This is a practice-oriented literacy course designed for non-experts to understand the core knowledge for "utilizing" AI well—covering everything from prompt engineering to security understanding, expansion, automation, and the structure of language models.

The capabilities of AI can expand infinitely depending on how you write your prompts. The benefits gained from collaborating with AI depend on the clarity of your questions and whether you provide sufficient background information. Now, please make full use of GPTs to perform efficient and purpose-driven tasks.

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.

The "How Far Can You Go with ChatGPT?" series: Creating a Stock Auto-Trading Application and Web Investment Reports!

This is a practical Vibe Coding course where you will create a Travel MBTI diagnosis web app using only AI prompts and complete it to a production-ready level by actually integrating the Korea Tourism Organization's public Open API. You will learn how to transform crude results into a high-quality web app using prompt-splitting techniques, and cover the entire process from API key security to deployment.

Have you ever regretted focusing only on notes instead of making eye contact, or failing to accurately record meeting details after an important client meeting? Don't worry anymore! In this lecture, we'll help you build your own CRM assistant using Vibe Coding to summarize key information with AI and neatly save meeting content. You'll learn how to drastically reduce cumbersome document creation time, maximize work productivity, and revolutionize customer management!

Learn how to automate daily tasks using AI tools and apply AI code to actual service development. We provide practical know-how that everyone from developers to non-developers can immediately apply to their own work environments.

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

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

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.

This is a foundational course utilizing GPT Image 2. You will understand the core principles of LLM-based image generation models and learn prompt design methodologies using the latest AI image generation tools. From basic concepts to detailed prompt engineering techniques that can be directly applied to practical tasks, this course will help you master practical AI image generation skills to enhance work productivity.

There may be people who have never taken my lecture, but no one has taken it only once. After teaching in person for so long, I have finally had the opportunity to create this online course. Since the commercialization of AI, I have produced and researched countless pieces of AI content. Based on that knowledge and experience, I have built a logical philosophy of my own and organized it into concepts that are easy for anyone to understand. For those who have been generating AI images without considering the underlying concepts, I will instill a proper foundation in you. Once you take this course, you will be able to create any AI image in the future. Have you never created an AI image before? That’s perfectly fine. You don't need any prior experience in AI image generation. By simply learning and practicing through this lecture, you can become an expert in AI content creation.