Gen AI — Introduction
What is Generative AI?
Generative AI creates new content—text, images, code, music—based on patterns learned from training data.
Key technologies
- Large Language Models (LLMs): GPT-4, Claude, Gemini
- Diffusion Models: DALL-E, Midjourney, Stable Diffusion
- Audio Models: Whisper, ElevenLabs
- Video Models: Sora, Runway
How it works
Input (prompt) → Model → Output (response)
- You provide a text prompt
- The model processes it
- It generates relevant output
Applications
| Application | Example |
|---|---|
| Chatbots | ChatGPT, Claude |
| Code generation | GitHub Copilot |
| Image creation | DALL-E, Midjourney |
| Writing | Jasper, Copy.ai |
| Research | Perplexity AI |
| Translation | DeepL |
Limitations
- Can produce incorrect information (hallucinations)
- Limited knowledge cutoff date
- May reflect biases in training data
- Not truly "thinking" or "understanding"
Getting started
# Simple API call
import openai
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)
Mini Practice
- Use ChatGPT for a task
- Compare responses from different models
- Experiment with different prompts
- Identify AI limitations
Up Next
Continue with Get Started - Your first AI application.
Related Topics
Frequently Asked Questions about Introduction
What is Introduction in Gen AI?
Introduction is a fundamental concept in Gen AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Introduction?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Introduction.
Why is Introduction important in Gen AI?
Introduction is essential for Gen AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.