Understanding AI
and Large Language Models
Learn what LLMs are, how Large Language Models work, and compare the latest AI models like GPT-4.1, Claude 4, Gemini 2.5, and Llama 4. This comprehensive tutorial covers everything beginners need to know about AI in 2025.
Learning Objectives
- Understand what LLMs are (GPT, Claude, etc.) and their role in modern AI
- Learn how LLMs work with simple explanations (no complex math required)
- Explore different types of AI tasks: Chat, Completion, and Embedding
- Understand AI limitations and responsible usage principles
Theory Section
What are Large Language Models (LLMs)?
Large Language Models (LLMs) are advanced AI systems trained on vast amounts of text data. They can understand and generate human-like text, making them incredibly versatile for various applications. Think of them as extremely well-read assistants who can help with writing, coding, analysis, and creative tasks.
How do LLMs Work? (Simple Explanation)
LLMs work by predicting the next word in a sequence based on patterns they've learned from training data. When you ask a question, the model:
- Breaks down your input into tokens (words or parts of words)
- Processes these tokens through many layers of neural networks
- Predicts the most likely next token based on patterns it has learned
- Generates responses one token at a time, like typing one word after another
💡 Key Insight: Token-by-Token Generation
Unlike humans who plan entire sentences, LLMs generate text sequentially:
This is why AI sometimes changes direction mid-sentence or creates inconsistencies - it doesn't "know" how the sentence will end when it starts.
Popular LLM Families
There are several major LLM families, each with unique strengths and characteristics. Understanding their differences helps you choose the right model for your specific needs.
OpenAI GPT Series
The pioneering commercial LLM family, known for versatility and broad knowledge.
- • GPT-4.1 (2025): Excellent coding capabilities
- • GPT-4o: Multimodal, replacing GPT-4 in April 2025
- • Strengths: Code generation, instruction following
- • API: Well-documented, extensive ecosystem
Anthropic Claude
Known for safety, nuanced responses, and following complex instructions.
- • Claude 4 Opus (2025): Industry-leading coding
- • Claude 4 Sonnet: Balanced performance
- • Strengths: Thinking mode, safety-focused
- • API: Simple interface, web search (US)
Google Gemini
Multimodal AI that can process text, images, audio, and video.
- • Gemini 2.5 Pro (2025): #1 on LMArena, thinking model
- • Gemini 2.0 Flash: Native multimodal I/O
- • Strengths: Audio/image generation, low latency
- • API: Google AI Studio, Vertex AI
Open Source Models
Free models you can run on your own hardware.
- • Llama 4 (2025): Scout, Maverick models
- • Llama 3.2: Vision models, edge deployment
- • Strengths: Privacy, customization, no API costs
- • Challenges: Requires GPU, licensing debates
Choosing the Right Model
For Production Apps:
- • Speed matters: GPT-4.1 nano, Gemini 2.0 Flash
- • Best quality: Claude 4 Opus, GPT-4.1
- • Cost-sensitive: Llama 4, open source models
For Specific Tasks:
- • Code generation: Claude 4, GPT-4.1
- • Long context: Llama 4 Scout, Claude 4
- • Multimodal: Gemini 2.0, GPT-4o
Types of AI Tasks
AI models can perform different types of tasks depending on how they're used. Understanding these task types is crucial for choosing the right approach for your specific needs.
💬 Chat Tasks
Multi-turn conversations where the AI remembers previous messages and maintains context.
Example:
User: "My name is John"
AI: "Nice to meet you, John!"
User: "What's my name?"
AI: "Your name is John."
Use cases: Customer support, personal assistants, tutoring
✍️ Completion Tasks
One-time text generation without conversation history. Each request is independent.
Example:
Prompt: "Write a haiku about coding"
AI: "Lines of code flow fast
Bugs hide in the shadows deep
Coffee fuels the night"
Use cases: Content generation, code completion, translations
🔢 Embedding Tasks
Converting text into numerical vectors (arrays of numbers) to measure similarity between texts.
Example:
"dog" → [0.2, -0.5, 0.8, ...]
"puppy" → [0.3, -0.4, 0.7, ...]
(Similar vectors = similar meaning)
Use cases: Semantic search, recommendation systems, clustering
Key Differences to Remember
- •Chat: Stateful conversations with memory of previous exchanges
- •Completion: Stateless, one-time text generation
- •Embedding: Text to numbers for mathematical comparisons
Practical Examples
Example 1: Testing Different LLMs
Try the same prompt with different LLMs to see how their responses vary:
Prompt: "Explain quantum computing in simple terms for a 10-year-old"
Notice how different models may emphasize different aspects or use varying analogies.
Example 2: Testing Prompt Variations
Try the same question with different prompt styles to see how it affects the response:
Simple: "What is Python?"
Detailed: "Explain Python programming language. Include:
- What it's used for
- Key features
- Why beginners like it"
Role-based: "You are a teacher. Explain Python
to a high school student."
Different prompt structures lead to different response styles and levels of detail.
Challenge Exercise: Knowledge Cutoff Test
Try this simple experiment with any AI chatbot (ChatGPT, Claude, etc.) to understand an important limitation:
Ask about recent events to test the AI's knowledge cutoff:
"What happened in the stock market today?"
"What's the weather forecast for tomorrow?"
"Who won yesterday's [sports team] game?"
"What's the current price of Bitcoin?"
Learning: AI models have a training data cutoff date. They cannot access real-time information unless specifically equipped with web search or other tools.
Why This Matters
When building AI applications, you'll need to:
- Provide current data through APIs or databases for real-time information
- Set user expectations about what the AI can and cannot know
- Design systems that combine AI capabilities with real-time data sources