Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Large Language Models (LLMs) Fundamentals in South Korea
Large Language Models (LLMs) Fundamentals is a comprehensive training course designed to help learners understand the core concepts, architecture, and applications of modern Large Language Models. Available in South Korea , the course introduces participants to LLMs, Generative AI, Natural Language Processing (NLP), transformers, tokens, embeddings, attention mechanisms, model training, fine-tuning, and inference . Learners explore how LLMs process language, generate responses, and support AI-powered applications such as chatbots, content generation, summarization, question answering, and intelligent automation. The course is suitable for students, developers, IT professionals, data professionals, business users, and AI enthusiasts who want to build foundational LLM expertise. Through practical learning and real-world examples, participants develop an understanding of how LLM technologies can be integrated into modern AI solutions. Enroll in LLM Fundamentals training in South Korea to strengthen your AI knowledge, understand emerging LLM technologies, and establish a foundation for advanced learning in Generative AI, RAG, AI agents, and LLM application development.
5 daysFoundation4.5 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
5 daysFoundation
- Module 1: Introduction to Large Language Models
- Understanding Large Language Models and their role in Generative AI.
- Evolution of Natural Language Processing and language models.
- Exploring capabilities, limitations, and common LLM use cases.
- Understanding the LLM ecosystem and applications in South Korea.
- Module 2: Natural Language Processing Fundamentals
- Introduction to text processing and language representation.
- Understanding vocabulary, tokens, tokenization, and text encoding.
- Exploring semantic relationships and language representations.
- Understanding how NLP supports modern LLM applications.
- Module 3: Transformer Architecture
- Introduction to the Transformer architecture.
- Understanding attention and self-attention mechanisms.
- Exploring encoder and decoder components.
- Understanding positional encoding and its role in language processing.
- Module 4: Tokens, Embeddings, and Context
- Understanding tokenization and token management.
- Exploring word, sentence, and contextual embeddings.
- Understanding context windows and sequence processing.
- Examining how context influences LLM responses in South Korea.
- Module 5: LLM Training and Pre-Training
- Understanding datasets and data preparation for LLM training.
- Exploring pre-training and language modeling objectives.
- Understanding computational requirements and training infrastructure.
- Exploring challenges associated with large-scale model training.
- Module 6: Fine-Tuning and Instruction Tuning
- Understanding the purpose of model fine-tuning.
- Exploring supervised fine-tuning and instruction tuning.
- Understanding parameter-efficient fine-tuning concepts.
- Identifying scenarios where customization can support specific AI applications.
- Module 7: LLM Inference and Generation
- Understanding the LLM inference process.
- Exploring text generation and decoding strategies.
- Understanding temperature, top-k, and top-p sampling.
- Examining factors that influence model responses and performance in South Korea.
- Module 8: LLM Applications and Use Cases
- Exploring conversational AI and virtual assistants.
- Understanding text generation, summarization, and classification.
- Exploring question answering and information extraction.
- Examining LLM applications across business and technology environments.
- Module 9: LLM Evaluation and Responsible AI
- Understanding approaches for evaluating LLM quality and performance.
- Exploring accuracy, relevance, consistency, and response quality.
- Understanding hallucinations, bias, privacy, and security considerations.
- Applying responsible AI principles to LLM applications in South Korea.
- Module 10: Practical LLM Applications and Future Trends
- Exploring practical workflows using modern LLM technologies.
- Understanding LLM-powered application development at a foundational level.
- Introduction to RAG, AI agents, and LLM application architectures.
- Exploring emerging LLM trends and pathways for advanced AI learning in South Korea.
Entry requirements and prerequisites
- Basic AI Knowledge – A general understanding of Artificial Intelligence and Machine Learning concepts is helpful for learners in South Korea.
- Generative AI Familiarity – Basic awareness of Generative AI and common AI applications is recommended.
- Programming Knowledge – Basic Python programming knowledge is beneficial but not mandatory for understanding LLM concepts and practical exercises.
- Basic Mathematics – Familiarity with basic mathematical concepts such as probability, statistics, and linear algebra can help learners understand selected LLM concepts.
- Computer Skills – Basic computer and internet skills are expected for accessing AI tools, course resources, and practical activities in South Korea.
- No Prior LLM Experience Required – Learners do not need previous professional experience with Large Language Models, transformers, or LLM development.
Career value
Reasons to choose this course
Build Strong LLM Foundations
Understand the core concepts, terminology, and technologies behind Large Language Models (LLMs) through structured training in South Korea.
Understand Transformer Architecture
Learn the fundamental principles of transformers, attention mechanisms, tokens, and embeddings that power modern LLMs.
Explore LLM Training Processes
Gain insights into pre-training, fine-tuning, instruction tuning, and inference workflows used in modern language models.
Learn Practical LLM Applications
Explore how LLMs support chatbots, content generation, summarization, question answering, and intelligent automation in South Korea.
Develop AI Technology Awareness
Understand the capabilities, limitations, and practical considerations of modern LLM technologies.
Prepare for Advanced AI Learning
Establish a foundation for progressing into RAG, prompt engineering, AI agents, LLM application development, and enterprise AI.
Advantages
Turn learning into practical capability
Understand LLM Capabilities and Limitations
Learn to recognize where LLMs perform effectively and where human validation, additional data, or specialized techniques may be required.
Interpret Model Outputs More Effectively
Develop knowledge of tokens, context, embeddings, and inference to better understand how LLMs produce responses.
Bridge Theory and AI Applications
Connect fundamental LLM concepts with practical solutions such as conversational assistants, text processing, and knowledge-based applications in South Korea.
Make Better Technology Decisions
Gain foundational knowledge that can help learners understand important considerations when selecting or working with different LLM technologies.
Strengthen AI Project Collaboration
Develop terminology and conceptual knowledge that supports communication between technical teams, business stakeholders, and AI professionals.
Create a Pathway to LLM Specialization
Build the conceptual base needed to explore advanced areas such as fine-tuning, RAG, LLMOps, evaluation, and production AI applications in South Korea.
Exam format & assessment
- Exam Level: Beginner–Intermediate – Large Language Models Fundamentals
- Exam Type: Online computer-based examination consisting of multiple-choice questions and practical LLM-based tasks in South Korea.
- Number of Questions: 40 questions
- Exam Duration: 90 minutes
- Passing Score: 70% (28 out of 40)
- Question Format: Multiple-choice questions covering LLM fundamentals, NLP, transformers, tokens, embeddings, attention mechanisms, training, fine-tuning, inference, and evaluation.
- Practical Assessment: Candidates may complete practical tasks involving LLM interaction, text generation, model parameters, prompt-based activities, and analysis of LLM outputs.
- Key Exam Topics: LLM architecture, transformer models, tokenization, embeddings, context windows, pre-training, fine-tuning, inference, decoding strategies, LLM applications, evaluation, and responsible AI.
- Prerequisites: Basic knowledge of Artificial Intelligence and Generative AI is recommended. Basic Python knowledge is beneficial but not mandatory.
- Assessment Platform: Online assessment through the StepMerit platform.
- Certification: Successful candidates receive a StepMerit Course Completion Certificate.
- Exam Preparation: Hands-on exercises, demonstrations, and practical LLM activities help learners prepare for the assessment in South Korea.
Bring your learning together
Course conclusion
The Large Language Models (LLMs) Fundamentals course provides a strong foundation for understanding modern LLM technologies, Generative AI, Natural Language Processing (NLP), transformer architecture, tokens, embeddings, attention mechanisms, model training, fine-tuning, inference, and LLM evaluation. Through practical examples and hands-on learning, participants develop the knowledge required to understand how LLMs generate content and power AI applications such as chatbots, question answering, summarization, content generation, and intelligent automation.
This course is suitable for students, developers, IT professionals, data professionals, business users, and AI enthusiasts seeking practical LLM training in South Korea. Learners also gain foundational knowledge that can support advanced studies in RAG, AI agents, prompt engineering, LLM application development, and enterprise Generative AI.
Target audience
- Students and Graduates
- AI and Machine Learning Professionals
- Software Developers
- IT and Technology Professionals
- Data Professionals
- Business Analysts
- Product and Project Professionals
- Content and Marketing Professionals
- IT Consultants and Solutions Professionals
- Researchers and AI Enthusiasts
- Career Switchers
- Business Owners and Entrepreneurs
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Trainer Experience: 5+ years of professional and training experience in AI, Machine Learning, Generative AI, NLP, and LLM technologies.
- Course Training Experience: Delivered 120+ hours of training focused on Large Language Models, transformer architecture, tokens, embeddings, attention mechanisms, prompting, and LLM application development.
- Candidates Trained: 180+ candidates including developers, data scientists, AI/ML professionals, technology professionals, students, and AI enthusiasts.
- Training Delivery: Conducted instructor-led online and classroom sessions combining theoretical concepts, live demonstrations, practical exercises, technical discussions, and hands-on activities.
- LLM Architecture Expertise: Provided practical training on transformer architecture, self-attention, context windows, tokenization, embeddings, and the fundamentals of LLM processing.
- Prompting Experience: Guided learners in developing effective prompts using roles, context, instructions, examples, constraints, and structured output requirements.
- Model Interaction: Trained candidates to work with LLM parameters, context management, response generation, model selection, and output evaluation.
- Banking Use Cases: Guided learners through practical LLM implementations involving banking customer support, knowledge retrieval, query classification, response generation, and output validation.
- Responsible AI Training: Covered responsible LLM usage, hallucination management, data privacy, security considerations, bias awareness, human oversight, and responsible deployment practices.
- Hands-On Projects: Supported candidates in developing practical LLM-based applications and workflows using real-world business scenarios.
- Assessment Experience: Evaluated candidates through quizzes, practical assignments, scenario-based exercises, prompt-development activities, and project-based assessments.
Documented learner outcomes
- Problem Statement – A banking organisation handles large volumes of customer queries related to accounts, transactions, loans, credit cards, banking policies, and services. Traditional support processes can require significant manual effort, while generic AI systems may produce inaccurate or unsupported responses. The challenge is to understand how Large Language Models (LLMs) can be used to build an AI-assisted banking support solution while maintaining accuracy, data privacy, security, and human oversight.
- How We Overcame the Problem – Learners analysed the banking support requirements and designed an LLM-based workflow using appropriate prompts, structured context, controlled inputs, and response validation. They learned how tokens, embeddings, transformer architecture, attention mechanisms, context windows, and model parameters influence LLM behaviour and applied these concepts to create more reliable AI responses.
- Overall Implementation – Learners implemented an LLM-powered banking assistance workflow covering customer query → prompt construction → relevant context → LLM processing → response generation → output validation → final response.
- LLM Fundamentals Implementation – Learners explored how transformer-based LLMs process text, predict tokens, understand contextual relationships, and generate natural-language responses.
- Tokenization and Context Management – Learners examined how banking queries are converted into tokens and how context length affects the information available to the model during response generation.
- Prompt Design – Learners created structured prompts for banking scenarios, including role instructions, customer context, response requirements, constraints, and expected output formats.
- Customer Query Classification – Learners designed workflows for categorising common banking queries such as account information, loan enquiries, card services, transaction questions, and general banking support.
- Knowledge-Based Response Generation – Learners used controlled banking information and contextual instructions to generate relevant responses while reducing unsupported or misleading information.
- Response Validation – Learners evaluated generated responses for accuracy, relevance, completeness, consistency, and compliance with predefined banking response guidelines.
- Model Parameter Exploration – Learners examined concepts such as temperature, token limits, context windows, and other model parameters to understand their impact on response quality and consistency.
- Responsible AI and Security – Learners considered banking-specific requirements including protection of sensitive information, privacy, access control, hallucination management, responsible AI usage, and human escalation for sensitive or high-risk requests.
- Evidence of Learning – Learners produced LLM concept exercises, prompt templates, banking query examples, generated responses, parameter experiments, validation checklists, workflow designs, and implementation documentation demonstrating their understanding of LLM fundamentals.
- Overall Learner Outcome – By completing the implementation, learners demonstrated practical understanding of LLM architecture, transformers, tokens, attention, embeddings, context windows, prompting, model parameters, response generation, evaluation, and responsible AI, and applied these concepts to a realistic banking AI-assistance scenario.
Helpful answers for planning your training
Frequently asked questions
LLMs are AI models that understand and generate human-like language for various applications.
You will learn LLMs, NLP, transformers, tokens, embeddings, training, fine-tuning, and inference.
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