Understanding LLM Concepts
Learn the key terms and concepts behind Large Language Models in simple terms
Parameters
What is it?
Think of parameters as the 'brain cells' of an AI model. More parameters usually mean the model can understand and generate more complex responses.
Think of it like...
Like having more neurons in your brain - generally more means smarter, but it also requires more energy.
Examples
- • GPT-4: ~1.7 trillion parameters
- • Claude 3: ~175 billion parameters
Training Data
What is it?
The information used to teach the AI model. It's like all the books, articles, and websites the AI 'read' during its education.
Think of it like...
Just like how you learned to write by reading books and seeing examples, AI models learn from massive amounts of text.
Examples
- • Books and literature
- • Web pages and articles
- • Code repositories
Context Window
What is it?
How much text the AI can 'remember' and work with at once. A larger context window means it can handle longer conversations or documents.
Think of it like...
Like your working memory - how much information you can keep in mind while having a conversation.
Examples
- • Claude 3: 200,000 tokens (~150,000 words)
- • GPT-4: 8,000-128,000 tokens
Training Method
What is it?
The technique used to teach the AI model. Different methods focus on different aspects like helpfulness, accuracy, or safety.
Think of it like...
Like different teaching methods - some focus on memorization, others on understanding and reasoning.
Examples
- • Reinforcement Learning from Human Feedback (RLHF)
- • Constitutional AI
- • Supervised Fine-tuning
Knowledge Cutoff
What is it?
The latest date of information the AI was trained on. The AI doesn't know about events after this date.
Think of it like...
Like a textbook that was published in 2023 - it won't have information about events in 2024.
Examples
- • GPT-4: September 2021
- • Claude 3: April 2024
Multimodal
What is it?
AI that can work with different types of input - not just text, but also images, audio, or video.
Think of it like...
Like a person who can read, look at pictures, and listen to music all at the same time to understand something.
Examples
- • GPT-4V (vision)
- • Claude 3 (text + images)
- • Gemini (text, images, audio)
Learning Tips
Start with the Basics
Focus on "Basic" concepts first. Understanding parameters, training data, and knowledge cutoff will give you a solid foundation.
Use the Analogies
The analogies help relate complex AI concepts to familiar ideas. Think about these comparisons when exploring models.
Learning Paths
Beginner
Start with the fundamentals - What is an LLM? Tokens and Context, Training Basics, Common Use Cases
Intermediate
Dive deeper - Architecture Types, Fine-tuning Methods, RLHF Process, Evaluation Metrics
Advanced
Cutting edge - Scaling Laws, Emergence Properties, Safety Alignment, Research Frontiers