Complete Guide to Prompt Engineering

Without a single formula or line of code, this book penetrates the essence of fundamental statistics needed 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.

Let's explore the types of applications or web services that can be built using LLMs.

This lecture is designed for understanding even if you have no prior knowledge of what Secure Coding (Security Coding) is. After covering basic security coding concepts, it focuses on web service security. This was a special lecture presented under the auspices of OO University. It presents over 160 source codes with rich comments, pinpointing core topics and techniques immediately applicable in practice.

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

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."

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. 🍽️

Now is the era of development using AI. To create better, more accurate code and documents by better utilizing AI, methods are needed, so we propose suitable ones.

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 methods for creating an AI judge by combining multiple agent frameworks such as CrewAI, LangChain, and AutoGen that enable the implementation of LLM-based multi-agent technology, along with knowledge graphs and reasoning systems.

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.

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

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!

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!

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

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

Practical Essential Skills for AI Use, Prompt Engineering Learn key skills to boost work productivity and business competitiveness with accurate, proper prompts.

The start of smart work - work intelligently anywhere, mobile or PC! Go beyond the limits of content creation with the most advanced Google AI tools available today.

AI Excel that automates perfectly for your work without company security concerns. From data preprocessing to visualization in just a few minutes, no coding knowledge required!

AI literacy refers to 'the ability for general users, not just technicians, to effectively utilize AI in their work and daily lives,' and 'the competency to critically evaluate AI.' This is a practical, literacy-focused lecture, designed for non-majors to understand, covering key knowledge for effectively 'utilizing' AI: from prompt engineering → understanding security → expansion → automation → to language model structure.

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.

Now is the era of development using AI. To create better, more accurate code and documents by better utilizing AI, methods are needed, so we propose suitable ones.

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

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.

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

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

This course is a question-focused training program that uses ChatGPT to directly design practical questions needed in the workplace and expand them into idea generation, report writing, and problem-solving. It is structured around hands-on practice, covering everything from creating good questions to team workshops, Why-based thinking, and question template automation, providing working professionals with question design and critical thinking skills they can immediately apply in the field.

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

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.

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 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.