Understanding LLM Application User Requirements Gathering and Analysis Methods (for Product Managers)

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.

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.

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.

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

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.

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.

Explains the reasons why LLM is necessary, the technical background, and basic concepts for LLM planners and LLM application planning managers.

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).

Explore the limitations of LLMs, the latest technologies to overcome them, and projected future technologies.

This is a practical user story writing course based on Jeff Patton's book "User Story Mapping." You will understand the characteristics of good user stories, learn effective writing methods, and develop skills that can be immediately applied to real-world tasks through various practical cases and examples. You can build professional expertise in creating and managing user-centered stories throughout the product development process.

This lecture covers how to use nine notes to improve your work efficiency and strategic planning skills.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

Learn how to utilize various local LLMs (Qwen, Gemma) and explore different techniques to efficiently evaluate and improve the performance of LLM systems.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

2024 Smartsheet Korean Latest Lecture. Let's learn how to use forms, reports, dashboards, etc. with practice.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

If you know that UT (usability testing) and user interviews are good, but don't know how to do them, gain confidence with this course!

This is a course to master the fundamental theories of LLM and the core technologies of LangChain and RAG. You can easily learn the latest AI technologies used in practice, starting from LLM basics!

Based on an accurate understanding of black consumers, appropriate countermeasures and strategies are acquired, enabling effective response to black consumers. Furthermore, it is designed to enhance the professionalism of service staff and improve job satisfaction.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

This is a course covering the GitHub notebooks and bonus content from <Build a Large Language Model from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Build a Large Language Model from Scratch> is the Korean translation of the bestseller <Build a Large Language Model (from Scratch)> (Manning, 2024) by Sebastian Raschka. This book provides a way to learn and utilize the operating principles of large language models by building a complete model starting from scratch with OpenAI's GPT-2 model.

This is for those who want to introduce and use JIRA and Confluence in their team projects.

This course provides UX principles and practical know-how necessary for planning and designing AI products. We will explore user-value-centered AI utilization methods, UX planning and design methods using AI, core UX principles and theories learned through everyday design cases, and UX research and data analysis methods for providing a better experience.

If you're wondering what UX Writing is and why it's important, you'll find out by looking at examples of successful apps.