🚀 Generative AI Fundamentals You Should Know
1. What is Generative AI?
Generative AI is a branch of Artificial Intelligence that creates new content by learning patterns from existing data.
Unlike traditional AI, which mainly predicts or classifies, Generative AI produces original outputs.
It can generate:
Text
Images
Audio
Video
Code
Music
3D models
Example
Input: "Write a Python function to sort a list."
Output: The AI generates Python code.
2. Traditional AI vs Generative AI
Traditional AI: Predicts outcomes
Generative AI: Creates new content
Traditional AI: Classification
Generative AI: Generation
Traditional AI: Fraud detection
Generative AI: ChatGPT
Traditional AI: Spam filtering
Generative AI: AI Image Generation
Traditional AI: Recommendation systems
Generative AI: AI Code Generation
3. Evolution of AI
Artificial Intelligence
→ Machine Learning
→ Deep Learning
→ Foundation Models
→ Generative AI
→ Large Language Models
Generative AI is built on Machine Learning and Deep Learning.
4. Real-World Applications
Healthcare
• Medical report generation
• Drug discovery
• Medical chatbots
Finance
• Risk analysis
• Report generation
• Fraud investigation
Software Development
• Code generation
• Bug fixing
• Documentation
Education
• AI tutors
• Quiz generation
• Content summarization
Marketing
• Advertisement copy
• Social media posts
• Product descriptions
Customer Support
• AI Chatbots
• Ticket summarization
• FAQ automation
5. Types of Generative AI
Text Generation
Example: ChatGPT, Claude, ChatGPT
Image Generation
Example: DALL·E, Midjourney, Stable Diffusion
Audio Generation
Example: Speech synthesis, AI voice cloning
Video Generation
Example: AI video creation, Talking avatars
Code Generation
Example: GitHub Copilot, AI coding assistants
6. What are Foundation Models?
Foundation Models are very large pretrained models trained on enormous datasets.
Characteristics:
• General-purpose
• Can perform many tasks
• Fine-tunable
• Support multiple applications
Examples: GPT, Llama, ChatGPT, Claude
7. What is an LLM?
LLM stands for Large Language Model.
An LLM is trained on billions of words to understand and generate human language.
Capabilities:
Question Answering, Translation, Summarization, Coding, Reasoning, Text Generation
Examples: GPT-4, Llama, Claude, ChatGPT
8. How does an LLM work?
Basic workflow:
User Prompt
↓
Tokenization
↓
Transformer Model
↓
Probability Prediction
↓
Generated Tokens
↓
Final Response
The model predicts one token at a time until the response is complete.
9. What is Tokenization?
Tokenization converts text into smaller units called tokens.
Example:
Sentence: "Generative AI is amazing"
Possible Tokens: ["Generative"] ["AI"] ["is"] ["amazing"]
The model processes tokens instead of raw text.
10. What are Embeddings?
Embeddings convert text into numerical vectors that represent semantic meaning.
