An LLM Primer for Planners and Leaders

An LLM Primer for Product Planners and Leaders

Although artificial intelligence (AI) and deep learning are reshaping industries across the board, it’s not easy to grasp the big picture of the concepts underpinning large language models (LLMs) or where they’re headed.

Many offerings on the market are either focused on complex, engineer-oriented explanations of research papers or, at the other extreme, limited to scattered prompt tips. This course strikes a balance, explaining the fundamental concepts, global trends, cost structures, and industry use cases that planners and business leaders need to know, in accessible language. Using visual diagrams and real-world examples, it takes you through a coherent story—from natural language processing (NLP) and word embeddings to transformers and attention, and from the competitive landscape among big tech companies after ChatGPT to the causes of hallucinations.

What This Course Covers

Fundamentals of Artificial Neural Networks and Deep Learning

To explain the big picture of artificial intelligence (AI), this lesson covers how artificial neural networks work. Using Google’s TensorFlow Playground, you can see for yourself how neural networks learn from data (through forward and backpropagation) and make inferences, while learning how they work, from perceptrons to multilayer perceptrons (MLPs).

Natural Language Processing (NLP) and Word2Vec

Natural language processing (NLP), which enables computers to work with human language, began with converting words into numbers. We examine the limitations of one-hot encoding and introduce the principles behind Word2Vec word embeddings: predicting a center word from the surrounding words in a sentence (CBOW), or predicting surrounding words from a center word (Skip-gram). We also introduce the concept of cosine similarity.

The Concepts of Transformers and Attention

We cover Attention, a core concept of the Transformer that ushered in the current LLM era. Using the example of finding what the pronoun ('It') refers to when translating from Korean to English, we explore which words a sentence pays attention to. We then bring the big picture together by reviewing the three types—encoder self-attention, masked decoder self-attention, and cross-attention—along with multi-head attention and the differences between encoder-based (BERT) and decoder-based (GPT) models.

The LLM Lifecycle: From Pre-Training to Knowledge Distillation

We look at the entire process of creating and advancing a model. We’ll identify where each technique fits in the four-stage pipeline, from pre-training and inference to post-training (fine-tuning), reinforcement learning from human feedback (RLHF), and knowledge distillation for model compression. Hands-on implementation of fine-tuning with actual open-source models will be covered in a follow-up course released later.

Trends and Applications: Big Tech Competition and Industry Use Cases

We examine how fast followers have pursued ChatGPT since its launch, the U.S.–China tech rivalry (DeepSeek, Qwen, and others), the latest frontier model landscape, and the business strategies of major tech companies. Through industry-specific use cases—from search, office automation, and coding to content editing, IT education, healthcare, and everyday life—we identify opportunities to apply these technologies in our organizations.

Limitations and Risks: Hallucinations, Copyright, and Personal Information

We also examine the challenges of using LLMs. Through concrete examples, you’ll see why hallucinations occur and how data conflicts can arise over time. We’ll introduce the basic roles of RAG (retrieval-augmented generation) and vector databases in mitigating hallucinations. Finally, we’ll cover limitations to understand before adoption, including the data-scarcity problem that prevents the latest technical information from accumulating, copyright disputes around open-source training, and privacy and ethical risks.

Curriculum roadmap: 3 sections, 50 lessons (3 hours 25 minutes total)

Section 1. The History of Language Models, AI Trends, and Hallucinations (21 lessons / 1 hour 33 minutes)

• Orientation and the definition of language models (LMs), and the principles behind Word2Vec (CBOW and Skip-gram)

• The Emergence of Large Language Models (LLMs) and Transformers, and the Seq2Seq Translation Model Architecture

• Visualizing Artificial Neural Network Training (Forward and Backpropagation) and Inference with TensorFlow Playground

• Global Tech Giants and Sovereign AI Trends, Adoption Cost (BEP) Structures, Field-Specific SOTA, and the Evolution of Coding AI and Pricing Plans

• Understanding the limitations of hallucinations through real-world examples (such as counting characters and changing to standard language), along with an overview of RAG and vector databases.

Section 2. The Scale and Cost of LLMs and the Concept of Attention (15 lessons / 55 minutes)

• The limitations of existing language models (N-gram and RNN) in predicting long sentences, and the evolution of models after the Transformer

• The distinction between training and inference, and the four-stage pipeline of pretraining, fine-tuning, RLHF, and knowledge distillation

• The evolution of model size (B), how to calculate GPU memory, quantization, and MoE/on-device AI specifications

• The relationship between context length (tokens) and pricing plans, causal and instruct models, and the concept of few-shot learning.

• The concept of attention and examples of pronoun tracking, multi-head attention, vision transformers (DLSS), and how Stable Diffusion works.

Section 3. Competition and Industry Use Cases After ChatGPT (14 lessons / 56 minutes)

• The ChatGPT revolution (surpassing 1 billion users) and fast followers catching up, the U.S.-China race for AI dominance (DeepSeek, etc.)

• The AI industry landscape and companies’ adoption and in-house integration strategies; a comparison of the business strategies of big tech companies (Anthropic, Google, and Meta)

• Industry-specific practical applications, from automating search, work, and coding to content editing, IT education, healthcare, and everyday life

• Limitations of LLMs: Hallucinations, a shortage of up-to-date technical data, and copyright, privacy, and ethical risks associated with open source

• Future Outlook (Two-Track Strategy: Large-Scale and Lightweight Models, Autonomous Driving VLA Models) and Epilogue

About the Knowledge Sharer

Ha Seok-jae | CEO of 2H Cube Co., Ltd. (2HCUBE)

2HCUBE is an AI education and consulting company that conveys the essence of advanced artificial intelligence technologies to the public and industry in the clearest, most practical terms.

• Completed bachelor's, master's, and doctoral coursework in Computer Science at Seoul National University.

• Delivered lectures on core AI technologies at major research institutes and corporations, including government-funded research institute ETRI and Samsung SDS Multicampus.

• Lectures at Korea University, Soongsil University, Myongji University, Chungnam National University, and others.

With the educational philosophy of conveying “the fundamental principles and context of technology, not just its surface trappings,” they help numerous leaders and practitioners navigate AI transformation.