Complete Guide to Prompt Engineering

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.

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.

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.

<Prompt Engineering> Direct lecture by author. Feel free to use ChatGPT, Bing, Bard, HyperCLOVA X, etc.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

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

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!

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

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.

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

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!

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.

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

You use ChatGPT, but haven't you found it difficult to explain why it gives certain answers? "I know terms like RAG, Agents, and Fine-tuning... but it's hard to explain them accurately." "I get tongue-tied whenever I hear LLM-related terminology." "Conceptual explanations are always vague during AI meetings." This course was created specifically for people like you. This is a theoretical course designed to help you understand LLMs not just as a 'tool,' but as a 'structure.' It doesn't teach you how to use ChatGPT or Gemini; instead, it establishes the foundation so you can explain exactly why they work the way they do.

Mastering ChatGPT: AI Use for Beginners - From Prompting to Content Creation and Programming

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

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.

A ChatGPT lecture planned with 2024's latest information. Based on large enterprise & finance field content, it covers hands-on 3.5/4.0 prompt practice and diverse generative AI tool use.

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

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice. Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop. Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios. Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services. 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph 👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo" 👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

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.

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

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.