Prompt Patterns for Developers

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 understand the entire development process from planning to code writing, deployment, and verification using artificial intelligence as a development tool, it helps them survive and thrive by acquiring the full-spectrum capabilities needed in the AI era.

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 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.

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.

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

This explains the foundational principles of large language models like ChatGPT with a focus on theory.

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!

A basic textbook and practical guide covering principles and practical uses of various generative AIs like ChatGPT and Claude, and for their effective introduction and utilization.

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!

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.

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.

It's structured to enable understanding of ChatGPT and text generation AI, learning effective real-world use, practice via diverse examples, and direct application.

"My ideas can make money? AI monetization, is it really possible?" Are you frustrated with the same monthly salary every day? Do you have brilliant ideas popping up in your head, but feel lost about how to actually get started? You hear that 'AI' is all the rage, but does it sound like a distant story for someone like you who isn't tech-savvy? What if you could turn your ideas into 'sellable digital products' without knowing a single line of code or having any marketing experience, and even create a system where money comes in while you sleep? This course is a practical guide that shows you exactly 'how' to do that.

Master prompt engineering perfectly, an essential skill for developers in the AI era.

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.

From server setup to blog and YouTube automation, build a complete AI workflow with n8n. We will practice creating a stable operating environment, covering Contabo VPS-based server installation, Ubuntu/Docker/Portainer environment setup, domain connection, and HTTPS security configuration. The course covers Search API and file server development using FastAPI and Docker, Airtable and n8n integration, building blog AI agents, and image/video automation. You will be able to complete a profit-generating automation strategy that can be immediately applied to actual service operations.

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

AI will not replace people. But 'you utilizing AI' will replace all who don't.

In this course, you'll master prompt engineering techniques directly applicable in real-world scenarios using the OpenAI API. You'll learn how to simultaneously boost efficiency and productivity in your daily life and work. Specifically, we'll help you gain interview confidence by automating the creation of customized resumes and cover letters for desired companies, as well as mock interview preparation.

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.

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

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.

How have others used ChatGPT in their daily lives? We are releasing actual conversation logs!