Large Language Models Explained
Large Language Models — or LLMs — are the technology behind ChatGPT, Claude, Gemini, and the AI revolution of the 2020s. In this lesson, we'll explore what they are, how they work, and why they've captured the world's attention.
What You'll Learn
By the end of this lesson, you'll understand what Large Language Models are, how they're trained, and what makes them powerful — and limited.
What Is a Large Language Model?
A Large Language Model is an AI system trained on vast amounts of text to predict and generate language.
Let's break down the name:
- Large: Billions of parameters (internal settings), trained on trillions of words
- Language: Deals with text — reading, understanding, writing
- Model: A mathematical system that captures patterns
The Simple Explanation
An LLM is like an autocomplete on steroids. Your phone predicts the next word in a text. An LLM does the same thing, but so well that it can:
- Write entire essays
- Answer complex questions
- Translate languages
- Write code
- Have extended conversations
The Scale That Changed Everything
What makes modern LLMs special is their sheer scale:
| Metric | Early Language Models (2010s) | Modern LLMs (2020s) |
|---|---|---|
| Parameters | Millions | Hundreds of billions |
| Training data | Gigabytes | Terabytes (trillions of words) |
| Training cost | Thousands of dollars | Millions of dollars |
| Capabilities | Basic prediction | Complex reasoning |
The breakthrough discovery: with enough scale, new capabilities "emerge" that smaller models don't have.
How LLMs Are Built
Step 1: Gather Massive Data
LLMs are trained on enormous text collections:
- Websites (a significant portion of the internet)
- Books (millions of titles)
- Academic papers
- Code repositories
- Conversations
- News articles
This data is cleaned and prepared for training.
Step 2: Pre-training
The model learns to predict what comes next in text. Over billions of examples, it builds a rich understanding of language:
Input: "The cat sat on the"
Model learns: "mat" is likely, "elephant" is unlikely
This is called pre-training because it creates a foundation for further refinement.
Step 3: Fine-tuning
Raw pre-trained models aren't very useful for conversations. They need fine-tuning:
- Instruction tuning: Teaching the model to follow directions
- Conversational training: Learning to have back-and-forth exchanges
- Safety training: Learning what content to avoid
Step 4: Reinforcement Learning from Human Feedback (RLHF)
Human reviewers rate model outputs. The model learns from these ratings to produce responses humans prefer:
- More helpful
- More accurate
- More appropriate
- Less harmful
This step is crucial for making LLMs useful and safe.
The Transformer Architecture
Modern LLMs are built on the Transformer architecture, introduced in a 2017 paper titled "Attention Is All You Need."
The Key Innovation: Attention
Traditional language models processed text sequentially (word by word). Transformers use attention — they can look at all words simultaneously and determine which words are most important for understanding each word.
Example: In "The cat sat on the mat because it was tired"
What does "it" refer to? The attention mechanism helps the model understand that "it" refers to "cat," not "mat."
Why Transformers Won
| Previous approaches | Transformers |
|---|---|
| Process one word at a time | Process all words at once |
| Slow to train | Highly parallelizable |
| Struggle with long text | Handle long context better |
| Limited understanding | Rich contextual awareness |
Every major LLM today — GPT, Claude, Gemini, Llama — uses the Transformer architecture.
Major LLMs You Should Know
GPT (Generative Pre-trained Transformer)
- Creator: OpenAI
- Powers: ChatGPT
- Notable versions: GPT-3 (2020), GPT-4 (2023), GPT-4o (2024)
Claude
- Creator: Anthropic (founded by former OpenAI researchers)
- Notable for: Longer context, thoughtful responses, safety focus
- Current versions: Claude 3, Claude 4 series
Gemini
- Creator: Google DeepMind
- Notable for: Multimodal (text, images, video), integration with Google services
- Powers: Google's AI features, Gemini app
Llama
- Creator: Meta
- Notable for: Open-source availability
- Impact: Enabled many research projects and smaller companies
Others
- Mistral (French company, strong open models)
- Command (Cohere, enterprise focused)
- Grok (xAI, Elon Musk's company)
What LLMs Can Do
Modern LLMs have impressive capabilities:
Language Tasks
- Writing (emails, essays, stories, code)
- Summarizing long documents
- Translating between languages
- Answering questions
- Explaining complex topics
Reasoning Tasks
- Solving math problems
- Logical reasoning
- Coding and debugging
- Analysis and synthesis
Creative Tasks
- Brainstorming ideas
- Drafting creative writing
- Generating variations
- Role-playing scenarios
What LLMs Cannot Do
Understanding limitations is crucial:
No Real-World Knowledge
LLMs only "know" what was in their training data. They:
- Don't know today's news (unless they have web access)
- Can't verify facts independently
- May have outdated information
No True Understanding
LLMs predict text patterns — they don't "understand" like humans:
- They can describe physics without knowing what falling feels like
- They can discuss morality without having moral feelings
- They can write about food without ever tasting anything
Hallucination
LLMs can confidently generate false information:
- Invented citations
- Non-existent facts
- Plausible-sounding nonsense
No Common Sense (Sometimes)
LLMs can miss obvious things humans would catch:
- Basic physical impossibilities
- Social context understanding
- When a question contains flawed assumptions
The Context Window
LLMs can only process so much text at once — this is called the context window.
| Model | Approximate Context Window |
|---|---|
| Early GPT-3 | ~3,000 words |
| GPT-4 | ~25,000 words |
| Claude 3 | ~150,000 words |
| Gemini 1.5 | ~1,000,000 words |
Context windows are expanding rapidly. This matters because:
- Larger context = handling longer documents
- Larger context = more detailed conversations
- Larger context = better understanding of your full request
Key Takeaways
- LLMs are AI systems trained to predict and generate text at massive scale
- They're built on the Transformer architecture using attention mechanisms
- Pre-training learns language patterns; fine-tuning makes them useful
- Major LLMs include GPT (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta)
- LLMs are powerful but limited: they can hallucinate and lack true understanding
- Context windows determine how much text LLMs can process at once
Quick Check
Before moving on, make sure you can explain:
- What does "LLM" stand for, and what does each word mean?
- What is RLHF and why is it important?
- Why can't you fully trust everything an LLM tells you?
What's Next
Now that you understand the technology, let's explore the practical side: the AI tools you can actually use today.