Understanding the Technology Behind ChatGPT - From LLM Fundamentals to Practical Applications
Series Overview
This series is a comprehensive 5-chapter practical educational content for systematically learning the fundamentals of Large Language Models (LLMs).
As AI assistants like ChatGPT, Claude, and Gemini become integrated into daily life, understanding the underlying technology is essential for everyone living in the AI era. This series covers everything from the basic principles of LLMs to implementation, evaluation, and practical applications.
Features:
- ✅ Modern Approach: Explanation of latest models including ChatGPT, GPT-4, Claude, LLaMA
- ✅ Practical Code: Over 30 implementation examples using Hugging Face Transformers
- ✅ Progressive Learning: Theory → Architecture → Implementation → Evaluation → Application
- ✅ Diagrams and Visualization: Understanding Transformer architecture and Attention mechanisms through diagrams
- ✅ Practical Perspective: Prompt engineering, fine-tuning, RAG, etc.
Total Study Time: 120-150 minutes (including code execution and exercises)
Learning Objectives
Upon completion of this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Ability to explain what LLMs are, their history and evolution
- ✅ Understanding the basic structure of Transformer architecture
- ✅ Knowledge of Self-Attention and Multi-Head Attention mechanisms
- ✅ Understanding the roles of tokenization and positional encoding
- ✅ Ability to explain the difference between pre-training and fine-tuning
Practical Skills (Doing)
- ✅ Ability to use LLMs with Hugging Face Transformers
- ✅ Design effective prompts (prompt engineering)
- ✅ Evaluate LLM output quality (BLEU, ROUGE, etc.)
- ✅ Implement simple fine-tuning
- ✅ Understand and implement RAG (Retrieval-Augmented Generation)
Application Ability (Applying)
- ✅ Propose appropriate LLM utilization methods for business challenges
- ✅ Understand and appropriately address LLM limitations and risks
- ✅ Track and understand latest LLM research trends
- ✅ Prepared to advance to more sophisticated LLM projects
How to Learn
Recommended Learning Sequence
🎯 Complete Master Course (All chapters recommended)
Target: Those who want to systematically learn LLMs, with basic machine learning knowledge
Path: Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
Duration: 120-150 minutes
Outcome: Comprehensive acquisition from LLM theory to implementation, evaluation, and practice
⚡ Practice-First Course
Target: Those who want to use LLMs immediately, practice-oriented
Path: Chapter 1 (overview only) → Chapter 3 (implementation) → Chapter 5 (practice)
Duration: 70-80 minutes
Outcome: Basic LLM understanding and practical application skills
🔍 Theory-Focused Course
Target: Researchers and engineers who want deep understanding of LLM principles
Path: Chapter 1 → Chapter 2 (detailed study) → Chapter 4 → related papers
Duration: 90-100 minutes + paper reading
Outcome: Deep understanding of Transformer theory and LLM research foundations
Prerequisites
Required Knowledge
- 📌 Python Basics: Basic syntax including variables, functions, lists, dictionaries
- 📌 Machine Learning Basics: Concepts of training and testing, loss functions, optimization
- 📌 Neural Network Basics: Multi-layer perceptron, activation functions
Recommended Knowledge (enhances understanding)
- 💡 Basic concepts of Natural Language Processing (NLP)
- 💡 Basics of deep learning frameworks (PyTorch, TensorFlow)
- 💡 Linear algebra basics (matrix operations, inner product)
Recommended Learning Resources (if prerequisites are lacking)
- → Supervised Learning Introduction Series - Python, NumPy, Pandas basics
- → Neural Networks Introduction - NN basics and PyTorch
- → Natural Language Processing Introduction - Basic NLP concepts
Chapter Details
Chapter 1: What is LLM
Learning Content
- Definition and characteristics of LLMs
- History of LLMs (from BERT, GPT, T5 to ChatGPT)
- Representative LLMs (GPT-4, Claude, LLaMA, Gemini)
- Transformer architecture basics
- Tokenization mechanisms (BPE, WordPiece)
- LLM use cases and limitations
Chapter 2: Transformer Architecture Coming Soon
Learning Content (planned)
- Detailed Self-Attention mechanism
- Multi-Head Attention structure
- Positional Encoding
- Feed-Forward Network layer
- Layer Normalization and residual connections
- Encoder-Decoder and Decoder-Only models
Chapter 3: LLM Implementation and Applications Coming Soon
Learning Content (planned)
- Hugging Face Transformers basics
- Loading pre-trained models and inference
- Prompt engineering techniques
- Few-Shot Learning, Zero-Shot Learning
- Adjusting text generation parameters (Temperature, Top-k, etc.)
- Fine-tuning basics
Chapter 4: LLM Evaluation and Improvement Coming Soon
Learning Content (planned)
- LLM evaluation metrics (BLEU, ROUGE, Perplexity)
- Human evaluation and benchmarks
- Bias and fairness issues
- Countermeasures against hallucinations
- RLHF (Reinforcement Learning from Human Feedback)
- Model compression and efficiency
Chapter 5: Practical LLM Applications Coming Soon
Learning Content (planned)
- Implementing RAG (Retrieval-Augmented Generation)
- LLM applications using LangChain
- Vector databases (Pinecone, Chroma)
- Building agent-based AI
- Deployment to production environment
- Cost optimization and security
Frequently Asked Questions (FAQ)
Q1: Can I learn LLMs without machine learning knowledge?
A: Basic machine learning knowledge (training, testing, loss functions, etc.) is recommended. If you lack this knowledge, we recommend first studying our "Supervised Learning Introduction Series" or "Neural Networks Introduction".
Q2: What is the difference between ChatGPT and GPT-4?
A: ChatGPT is an application name, with GPT-3.5 or GPT-4 as the underlying model. GPT-4 is larger and more capable than GPT-3.5, enabling more complex reasoning and longer text understanding. This is explained in detail in Chapter 1.
Q3: What do I need to run the code?
A: Python 3.8 or higher, and libraries like transformers, torch, numpy, pandas are required. Using Google Colab eliminates the need for environment setup and provides free GPU access. Setup instructions are provided in each chapter.
Q4: Do I need a high-performance GPU to run LLMs?
A: For inference (using existing models), smaller models can run on CPU. GPUs are recommended for large models or fine-tuning, but you can try for free using Google Colab or Hugging Face Inference API.
Q5: What should I do after completing this series?
A: You can proceed to more specialized series such as "Transformer Details," "Fine-tuning in Practice," or "RAG Implementation." Also, applying LLMs to actual projects deepens understanding.
Q6: Are there restrictions on commercial use of LLMs?
A: Licenses vary by model. GPT-4 must comply with OpenAI API terms of service, LLaMA had research-only restrictions, but LLaMA 2 and later allow commercial use. This is explained in detail in each chapter.
Let's Get Started!
Ready to begin? Start with Chapter 1 and explore the world of Large Language Models!
Update History
- 2025-12-01: v1.0 Initial release (Chapter 1 only)
Your LLM learning journey starts here!